CAPITAL REQUIREMENTS FOR INDIAN BANKS: AN EMPIRICAL ANALYSIS · Capital Requirements for Indian Banks: An Empirical Analysis 3 top 20 borrowers from banks, which included large infrastructure

ASIAN DEVELOPMENT BANK

CAPITAL REQUIREMENTS FOR INDIAN BANKS: AN EMPIRICAL ANALYSISAnupam B. Rastogi and Vivek Rao

ADB SOUTH ASIA WORKING PAPER SERIES

NO. 31

November 2014

ADB South Asia Working Paper Series

Capital Requirements for Indian Banks: An Empirical Analysis

Anupam B. Rastogi and Vivek Rao

No. 31 November 2014

Anupam B. Rastogi is from the School of Business Ad aidnI , 650004 iabmuM ,ytisrevinU SMIMN ,noitartsinim

Vivek Rao is Principal Financial Sector Specialist of the Public Management, Financial Sector, & Trade Division,

ASIAN DEVELOPMENT BANK

South Asia Department, Asian Development Bank

Asian Development Bank 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org © 2014 by Asian Development Bank October 2014 ISSN 2313-5867 (Print), 2313-5875 (e-ISSN) Publication Stock No. WPS147035-2 The views expressed in this publication are those of the author and do not necessarily reflect the views and policies of the Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. By making any designation of or reference to a particular territory or geographic area, or by using the term “country” in this document, ADB does not intend to make any judgments as to the legal or other status of any territory or area. ADB encourages printing or copying information exclusively for personal and noncommercial use with proper acknowledgment of ADB. Users are restricted from reselling, redistributing, or creating derivative works for commercial purposes without the express, written consent of ADB. Unless otherwise noted, “$” refers to US dollars. Printed on recycled paper

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CONTENTS

ABSTRACT ...................................................................................................................... ................................................. iv

I. INTRODUCTION .................................................................................................................................................. 1

II. STRUCTURAL FEATURES OF INDIAN ECONOMY AND THE FINANCIAL SECTOR : 1951–2012 .............................................................................................. 1

A. Inflation ................................................................................................................................................................. 1

B. Output ................................................................................................................... .............................................. 2

C. Investment ......................................................................................................................................................... 2

D. Consumption .................................................................................................................................................... 2

E. Competitiveness ............................................................................................................................................... 2

F. Labor Market ..................................................................................................................................................... 3

G. Banking ector .................................................................................................................................................. 3

III. ANALYTICAL FRAMEWORK OF A MACROECONOMIC MODEL .............................................. 6

A. Specification and Estimation ....................................................................................................................... 7

B. External Trade, Government Budget Constraint, and External Debt ......................................... 10

C. Portfolio Selection .......................................................................................................................................... 11

IV. THE MODEL ESTIMATION ............................................................................................................................ 13

A. Three-Stage Least Squares System Estimation ................................................................................... 13

B. Static-Deterministic Simulation of the Model .................................................................................... 17

C. Dynamic-Deterministic Simulation of the Model ............................................................................. 18

V. FINANCIAL MODELING OF INDIAN BANKS ...................................................................................... 22

A. Disclosure and Reporting Requirements .............................................................................................. 22

B. Risk Management .......................................................................................................................................... 22

C. Impact of Basel III on Indian Banks ......................................................................................................... 23

VI. CONCLUSION AND KEY RESULTS .......................................................................................................... 25

REFERENCES ....................................................................................................................................................... 26

APPENDIX 1 ................................................................................................................... ....................................... 28

APPENDIX 2 ................................................................................................................... ...................................... 29

APPENDIX 3 ................................................................................................................... ....................................... 33

APPENDIX 4.................................................................................................................... ..................................... 67

APPENDIX 5 ................................................................................................................... ...................................... 70

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TABLES AND FIGURES

TABLES1. Balance Sheets ............................................................................................................................................................ 112. System of 10 Equations ............................................................................................................................................133. System Estimation Result ...................................................................................................................................... 144. System with the Estimated Coefficients ...........................................................................................................155. System Variables ........................................................................................................................................................ 166. Model with System Equations and Identities ................................................................................................. 167. Baseline Data from 2011 to 2018 (in Rs billion) .............................................................................................. 188. Annual Growth Rate from 2011 to 2018 (Baseline Forecast) .................................................................... 189. 2011 to 2018 Variables Forecast after 10% REER depreciation (in Rs billion) ..................................... 2010. Annual Growth Rate after 10% fall in REER ................................................................................................... 2011. Forecasts and Annual Growth Rate Variables ................................................................................................ 2112. Some Key Differences between Global Basel II Accord and RBI Norms ............................................2213. RBI Plan for Shifting to Advance Approaches ...............................................................................................2314. CAR requirements for Large Public Sector Banks (baseline and simulation results) .....................24

FIGURES1. RBI’s Current Monetary Policy Framework ....................................................................................................... 52. System Residuals ........................................................................................................................................................153. Graph of Simulated Endogenous Variables ..................................................................................................... 174. Dynamic-Deterministic Simulations ................................................................................................................. 185. Simulation 1 REER ..................................................................................................................................................... 196. Simulation 2 ................................................................................................................................................................. 21

ABSTRACT The paper analyzes the capital requirements for Indian banks on its exposure to the infrastructure sector’s estimated $1 trillion investment requirement from 2013–2018 with 50% programmed to come from the private sector. Current bank exposure to infrastructure is around 17% of total assets which makes meeting this requirement a challenge. The paper also addresses the implications to Indian banks of this proposed expansion in infrastructure lending given the constrained from the implementation of Basel III requirements we well as the emerging risks due to the slowdown of GDP growth and currency depreciation. The study is divided into two parts, (i) developing a macroeconometric model that forecasts bank credit and deposits over 2013–2018 which feeds into a financial model, and (ii) analyzing the implications on bank balance sheets and the challenges in meeting infrastructure financing requirements. The results point to the need for significant capital infusion, at least in public sector banks.

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1. INTRODUCTION 1. The objective of this study is to determine the capital adequacy requirements of Indian banks for fulfilling the infrastructure investment targets overt the 2013-18 period. The paper analyzes the impact on bank balance sheets from a variety of factors including the currency depreciation, decline in gross domestic product (GDP) growth through a slowdown in investments. The study is conducted in two parts—(i) developing and estimating a macro econometric model for the Indian economy with the objective of forecasting bank deposit and credit growth, and (ii) mentioned forecasts feeding into bank specific financial models to estimate the potential impact on bank variables. The forecast period is over the Twelfth Five Year Plan, FY2013–FY2017. 2. The paper follows the following plan. Section II describes the structural features of the Indian economy and the financial sector from 1951 to 2012. Section III provides an analytical framework of a macro-model and Section IV estimates the model and explains the results. The model is estimated over the period 1951–2012. Section V describes the model simulation and validation. Section VI explains the banking sector model and the models for the individual banks driven by their balance sheet, ownership structure, and market share in the credit market. Finally, Section VIII concludes the study. II. STRUCTURAL FEATURES OF INDIAN ECONOMY

AND FINANCIAL SECTOR 3. In this section an econometric model of the India economy is developed, combining new classical theory with appropriate national institutional aspects. The objective is to forecast major macroeconomic variables while ignoring complexities arising from the scale of the informal sector, quantity rationing, and administered prices. In the modeling exercise in Section III, the model seeks to explain the decisions of agents as reflected in the data. Inflation, output investment, consumption, and employment are considered to be the main macroeconomic indicators along with competitiveness and real wages. Hence a brief overview of these is provided in this section. A. Inflation 4. The central bank, namely the Reserve Bank of India’s (RBI) monetary policy stance has focused on the twin objectives of containing inflation and facilitating growth. Khundrakpam and Das (2011) examine the relative response of food and manufactured products prices to change in interest rate and money supply. They find that, in the long-run, variations in money supply impact prices of both food and manufactured products but the impact of money supply is more on food than on manufacturing. The credit transmission mechanism in India has been effective and monetary policy has an impact on the finance premium incurred by companies. This impacts their demand for credit and hence economic activity and further on investment, output, and inflation.1 The consumer price index and wholesale price index are proxy measures for inflation. However, for the economy as a whole, GDP deflators whether at market prices or factor costs are more appropriate measures of the inflation. �

1 The impact of change in repo rate on the weighted average cost of capital (WACC) depends on the stage at which the

firm is operating. For example, if it is incurring huge capital expenditure, the impact on WACC is significant. �

2 ADB South Asia Working Paper Series No. 31

B. Output 5. From the 1950s to the early 1980s, India grew at an average annual rate of 1.2% on a per capita basis, implying a doubling of per capital income every 57 years. However, between FY2003 and FY2007, per capita income grew at 6.6% annually implying that per capital income would double in less than 11 years. The acceleration of economic growth in the 1980s was likely due to economic liberalization and dismantling of capital restrictions. Except during monsoon failures in FY1966–FY1968 and FY1986–FY1988, total output grew at an average rate of 4%. In the early 1980s it improved to around 5.3%, while agriculture grew at an average rate of 2.6% per year. As a result, its proportion in total GDP fell from 50% in the 1950s to 35% in the 1980s. Exports remained static until the mid-1970s at around 6.5% as a result of the prevailing economic philosophy of self- ciency. C. Investment 6. The investment rate (fixed capital and inventory) increased from 24% of GDP in FY2000 to 38% of GDP in FY2008. While the investment rate inched up following liberalization from about 22% in the mid-1990s, it has since remained constant for nearly a decade. The increase in investments was due primarily to acceleration in private investment which increased from 5% of GDP in FY2001 to 16% of GDP in FY2008. After decades of controls and protection from domestic and global competition, the private sector benefited from opportunities a rded by the gradual opening up of the economy. 7. By FY2000, services sector became an important growth driver. Most software businesses were start-ups, and enjoyed no protection and competed globally. Indian manufacturing changed its business model and emerged stronger by way of expanding their capital with lower leverage and accumulating cash. Private investment inched up to 6.8% of GDP in FY2004, then jumped to 10.8% in the following year and continued to rise to 16.1% in FY2008. The expansion of servicesand the emergence of retail finance helped shape the domestic market in ways that were supportive to manufacturing. D. Consumption 8. Real growth in private consumption increased substantially from about 5% per annum in 1997 to 2003 to over 8% per annum in 2005 to 2009. Even in the crisis year of FY2009, private final consumption expenditure grew by 4.3%. However, the acceleration in the volume of private final consumption expenditure played a secondary role in increasing the growth rate. Of the approximately 4% increase in the GDP growth from about 5% to 9%, private consumption expenditure accounted for about 1.5% while investment contributed about 4%.2 E. Competitiveness 9. One of the reasons for the slow growth in trade until the mid-1970s was the over-valuation of the Indian rupee (RS), making Indian products uncompetitive. Indian policy-makers followed a strategy of industrialization through import substitution. Only in the late 1970s and 1980s did the government try to keep the rupee competitive against a basket of currencies (Nayyar, 1988). Current account convertibility was achieved in 1994 (Ahluwalia et al 2002). The real ive exchange rate,

2 Increase in government expenditure especially for consumption crowds out private sector activity (Callen 2001). In this

context, when fiscal deficits have increased beyond 3% of GDP, stresses in the private sector have led to current account deficit in India.

Capital Requirements for Indian Banks: An Empirical Analysis 3

which takes into account domestic inflation in India, and is an important determinant of the competitiveness of Indian exports, has depreciated by about 11% since mid-2011.3 F. Labor Market 10. India from both hidden unemployment and under-employment. The reasons include slow growth in investment, distortions in factor markets, and capital intensive manufacturing (Balasubramanyam, 1984). India is not creating enough employment while having some of the most comprehensive labor laws in the world. The main culprit in the organized sector is the Chapter 5B of the 1947 Industrial Dispute Act, which bars establishments with more than 100 workers from laying employees without government permission. This deters employment and encourages capital substitution.4 11. Wages. Real wages in the agriculture sector remained static until 2005. It has been rising at a around 10% per annum after the government introduced National Rural Employment Guarantee Program (NREGA). In the nonagriculture sector, real wages declined until the late 1970s, and grew at 1.6% in the 1980s. In the organized sector, real wages grew at a rate of 3% to 4% (Shaw, 1990). The high growth rate of real wages contributes to capital intensive manufacturing. G. Banking Sector 12. The public sector uses a portion of bank resources namely the statutory liquidity ratio (SLR), which direct bank resources to the government.5 The government also directs banks to lend to priority sectors.6 The priority sector allocation of credit at concessional rates conflicts with monetary policy objectives. As priority sectors do not get a ected during credit squeeze, the burden of adjustment falls on medium and large industry and weakens the tiveness of monetary policy. The effectiveness of monetary policy on bank lending depends on (i) existence of borrowers who depend on banks, and (ii) impact of monetary policy on willingness of banks to lend. Small firms are less informed of credit conditions and have fewer financing options than large firms, and thus, the dependence of small firms on bank credit is more. 13. Infrastructure projects in India depend on bank loans for over 80% of their debt. The RBI indicates that the compound annual growth rate (CAGR) of bank loans to infrastructure has been in excess of 40% per annum.7 Further, with a current exposure of around $100 billion to the infrastructure sector, banks are fast approaching sector exposure limits.8 With private sector debt requirements at

3 India's external debt has remained within manageable limits as indicated by the external debt-GDP ratio of 19.7%% and

debt service ratio of 6.0%% in 2011-12. Openness of the economy measured in terms of trade/GDP ratio and financial openness measured in terms of FDI/GDP ratio trended upward since 1991 (Ahluwalia et al 2002).

4 Where labor laws and labor unions do not ect an entrepreneur’s decisions, intensive labor techniques are used (Shaw, 1990).

5 The SLR is an asset requirement to be held by each bank against its liabilities. These assets include “approved” securities as well as liquid assets such as gold and cash.

6 Indian government intervention in allocating credit both by directing commercial banks to lend to priority sector and special development banks to make priority loans has a direct bearing on the profitability of banks. Indian bank term lending, about 40% of total credit outstanding remained skewed towards priority sectors such as agriculture, transport, and small-scale industry and term lending to trade and large industries remained marginal.

7 Reserve Bank of India. 2010. Financial Stability Report. Mumbai. 8 As core CRAR of banks under Basel I and II stood at 13.0% and 14.2% as of FY2011, higher than the 6% level stipulated by

RBI, infrastructure financing limits were relaxed in July 2011. These included (i) relaxing the limits on bank investments in corporate bonds to 20% of total non-statutory liquidity ratio investments; and (ii) raising the single borrower limit from 15%–20% of bank capital and group exposure to 40%–50%, provided the additional credit exposure is on account of extension of credit to infrastructure projects. Relaxations increased concentration and bank exposures to the top 7 of the top 20 borrowers from banks, which included large infrastructure players, exceeding 40% of net worth and in a few cases breaching limits.


around $350 billion for infrastructure over the 2013–2018 period, banks will have to significantly expand their infrastructure portfolios while facing constraints. First, the share of infrastructure financing as a portion to total bank lending increased from 1.8% in FY2001 to over 14% in FY2011, suggesting an increase in concentration risk reaching close to maximum exposure limit of 15%.9 Second, long-term infrastructure loans increase the asset-liability management (ALM) mismatches in banks, due to short-term deposits, which can lead to macroeconomic vulnerability.10 Third, the mismatch in tenor is passed on to the borrower with loan rates being reset every year, increasing project risk which can result in credit risk for banks. 14. Reserve requirements. The two reserve requirements, namely, cash reserve ratio (CRR) and SLR (currently at 23%) are the instruments which control liquidity and allocate resources between the public and private sectors.11 The SLR has reduced the power of banks to provide private sector credit and has become an instrument to provide low cost resources to the government. Thus, SLR requirements affect credit creation by reducing the amount available for lending but did not have a deflationary impact on the money supply. The CRR (currently at 3%), on the other hand, has a deflationary impact on money supply as a certain proportion of bank liabilities had to be kept with RBI as cash balance.12 15. Interest rate policy. The monetary policy framework in India has evolved over time.13 During 1971–1990, the focus of monetary policy was on credit planning. To raise resources for the government, the SLR was progressively increased from the statutory minimum of 25% of banks’ net demand and time liabilities (NDTL) in 1970 to 38.5% by 1990. To neutralize the inflationary impact, CRR was gradually raised from its statutory minimum of 3% to 15% of NDTL during this period.14 Financial liberalization in the 1990s led to a shift in the paradigm with increasingly market-determined interest and exchange rates. By the second half of the 1990s, in its liquidity management operations, the RBI moved away from direct to indirect market-based instruments.15 The liquidity adjustment facility (LAF), operated through overnight fixed rate repo and reverse repo from November 2004, has acted as the key indicator of short-term macroeconomic liquidity.

9 Reserve Bank of India. 2011. Report on Trend and Progress of Banking in India 2010–2011. Mumbai. 10 RBI data indicates that as of end September 2010, the ALM positive gap in >5 year bucket constituted 42% of the total

ALM positive gap, followed by 3–5 years (31%) and 1–3 years (27%). 11 The CRR is a minimum cash balance which the banks have to hold with the RBI.

12 The ratio of bank investment in approved securities to aggregate deposits has remained range bound at around 30%, significantly higher than the minimum required under the SLR of 23%. The higher allocation to government securities may be due to a higher risk perception or non-availability of quality lending opportunities to the private sector or both. The ratio of credit to deposits, however, increased from 74.3% in Q1 of FY2011 to 76.7% in Q3 of FY2012 allowing the banks to maintain a higher credit growth than would otherwise have been feasible given the growth in deposits. The capital to risk-weighted assets ratio (CRAR) remained well above the RBI's stipulated 9% for Indian banks a whole in FY2012. Also, the CRAR (Basel II) at the system level improved marginally at and stood at 14.24% as of end-March 2012, as compared to 14.19% as of end-March 2011. RS12,000 was infused in seven public sector banks in FY2011 to enable them to maintain a minimum tier 1 CRAR of 8% and to increase shareholding of the government in these banks.

13 Report of the Working Group on Operating Procedure of Monetary Policy (Chairman: Deepak Mohanty) http://www.rbi.org.in/scripts/BS_PressReleaseDisplay.aspx?prid=24063

14 The 1980s saw the adoption of monetary targeting framework based on the recommendations of Chakravarty Committee (1985). Under this framework, reserve money was used as operating target and broad money (M3) as an intermediate target. A number of money market instruments such as inter-bank participation certificates, certificates of deposit, and commercial paper were introduced based on the recommendations of Vaghul Committee (1987).

15 The CRR and SLR were brought down to 9.5% and 25% of NDTL of banks by 1997.


16. The current operating framework is illustrated in the figure below. Under this framework, since May 2011, the RBI has announced explicitly that weighted average of the overnight call rate is the target rate and repo rate is single policy rate. Increase and decrease in the repo rate signals the policy stance.16 The repo rate is the only one independently varying policy rate. Since the call rate is determined in the overnight market for reserves, the RBI has the maximum influence over the level of call as it is the monopoly supplier of reserves. A new marginal standing facility (MSF) is also available, under which, banks could borrow overnight at their discretion up to 1% of their respective NDTL at 100 basis points above the repo rate. The liquidity corridor is defined with a fixed width of 200 basis points. The repo rate is in the middle of the corridor, with the reverse repo rate 100 basis points below it and the MSF rate 100 basis points

Source: Reserve Bank of India, 2014.

above it. MSF provides a safety valve against

Figure 1: RBI's Current Monetary Policy Framework

unanticipated shocks.

17. According to Bhattacharya et al. (2011), the interest rate channel in India is weak and the most effective mechanism is the exchange rate channel. According to their long-run co-integrating relationship, an increase of 100 bps in the call money rate has a negligible impact on industrial production (their activity variable) and a reduction of only 1 bps in inflation. In comparison, 1% currency depreciation increases inflation by 20 bps. The impact of interest rate on inflation is not direct, but through the exchange rate channel as higher interest rates leads to currency appreciation which then impacts inflation. Similarly, Bhalla (2011) is skeptical about monetary policy’s ability to contain inflation.17

16 The RBI normally sets the policy rate in its policy announcements, normally made eight times a year. 17 According to Bhalla (2011), “there is no evidence to link the decline in India’s inflation rate post 1996 to Indian monetary

policy, while there is a lot to link the decline in India’s GDP growth rate to this very same policy”.

6 ADB South Asia Working Paper Series No. 31 �

III. ANALYTICAL FRAMEWORK OF THE MACROECONOMIC MODEL 18. The model assumes three economic agents namely consumers, firms, and the government. The model draws its theoretical framework from the Minford and Walters (1989) and the Landon (1990) model, with adaptive expectations. The agents are assumed to be rational and expectations about future variables are adaptive.18 The main emphasis in the model is on the supply side, that is, the effects of taxes, subsidies, and other government interventions which alter relative prices and influence productivity. The supply side in this model is also related to the labor market. The aggregate supply curve is given by the output of producers, who set prices at a mark-up over variable cost. In the economy, real wages are flexible; the amount of labor employed clears the labor market which depends on the aggregate output.19 The real sector of the economy has two markets–goods and labor. The demand side is derived from the portfolio allocation decision of the agents. The private sector’s portfolio consists of physical and financial assets; the demand for a stock of physical assets and non-durable consumption is derived from their portfolio choice (Matthews and Minford [1980], Rastogi [1989]). The variation of the private sector’s portfolio provides the stimulus for private investment, inventory build-up or run-down, and increased or decreased purchase of consumer goods. Imports respond to the current income and the real exchange rate and exports are a function of the level of world activity and the real exchange rate. 19. There are two further economy-wide markets in the model—a money market and a bond market. The model is completed by a set of portfolio allocation equations which determine financial asset holdings and rates of return. Domestic assets markets are perfectly competitive and agents are assumed to be risk neutral. Short- and long-term domestic securities are assumed to be risk neutral. Agents in the private sector may hold foreign money and bonds, but for modeling purposes, they are formally treated as holding government bonds, proceeds of which are invested, in turn, in foreign bonds. 20. A stylized version of the model, ignoring for simplicity’s sake the two sectors, is as follows: Supply side:

� �rilky f ��

� ; � 2( , , , )l f k rw y tax ; � �ud,Ep - p y, l, k, f rw 1-3� (i)

Goods market: � �w r, fC 4� ; � �w r, , fg

5�� ; � �wt e,f xp

6� ; � �y e,fim

7� (ii)

�

18 This is based on Sneessens and Dreze, 1986), which indicates that changes in relative factor prices affects the capital-

labor ratio slowly (This suggests that the technique of production changes slowly to changes in the factor price structure because of adjustment costs).

19 A large proportion of the public sector output is non-marketable and prices of the marketed output are administered. This may underestimate true output value of the public sector output. This is true, however, in all economies.

Capital Requirements for Indian Banks: An Empirical Analysis 7 �

Assets market: � �R, y f m 8� (iii)

� � m � 1d + �ms (supply of money) (iv)

�� = �(x – d – �y) – q�p – �p (balance sheet constraint) (v)

r = rf – Ee-1 + e (efficient market condition using adaptive expectations) (vi)

R = r + Ep-1 – p (nominal-real interest rate using adaptive expectations) (vii)

�Where: c = non-durables consumption d = cyclically adjusted government deficit including interest payments as a fraction of GDP e = real exchange rate E-1 = expectations formed at time t – 1 g = stock of goods im = import volume k = stock of capital l = employment m = money supply p = GDP deflator q = stock of financial assets R = nominal interest rate r = real interest rate rf = real foreign interest rate ri = index of rainfall rw = real wages tax = indirect taxes ud = union density w = private sector stock of wealth wt = world trade volume x = government expenditure xp = export volume y = output � = average tax-rate � = private sector stock of financial assets A. Specification and Estimation 21. In this section, a model for the Indian economy is developed which can be estimated using time-series data. In specifying the equations, the structure of the model outlined below is used, while embedded in the specification is the existence of capital control, effective capital mobility, and risk premi .


1. Labor Demand 22. The long-run production function for output is assumed to be of the Cobb Douglas form: � �� 1L K Y (viii) 23. Further, it is assumed that the agricultur sector has a cost constraint: �

rK Lwc �� (ix)

Where w is the wage rate and r is the cost of capital. 24. The first-order conditions to maximize output subject to L, labor employed in the economy, and K, capital stock can be derived, and solved for L in the long-run to get the basic labor demand equation:

� r log Y log w log L log 321o �� (x)� 25. As there is corporation tax, sales tax, and excise tax in the sector, the effective demand for labor would depend on the level of these taxes. It is assumed that these tax variables are the shift factors in the labor demand function of the economy. The functional form of L equation can be written as: L � ��o + �1 log w + �2 log Y + �3 log r + �4 corp + �5 sales + �6 excise� (xi)�

2. Labor Supply 26. Wages in the economy are set by domestic “competition”—there is a “competitive” wage rate that an employer can just afford to pay and remain viable. This wage rate is affected by input prices. These wages, in turn, determine the output price and thus, demand for output. Employment in the sector depends on output and wages. 27. A perfectly competitive labor market is assumed and the main determinants of the market supply of labor are the real wage rate and the size of population.20 Thus, the total supply of labor is modeled as:

� �wpop W/p, g L 1s � (xii)

28. Equating labor supply and the functional form of labor demand � �t

1d z r, y, W/p, hL � , where zt is

a set of variables which influence labor demand, the market clearing average real wage in the functional form can be written as:

� � � � wpop,z r, y, h W/p t2� (xiii)

20 For completeness formal modeling of preferences of leisure and work would be desirable, but is not appropriate for the

aggregated model developed here.


29. Union power at the macroeconomic level can be visualized as affecting real wages through the bargaining process. Union density (UD) can be employed as a proxy for this bargaining process. The functional form of real wages in the economy can be written as: �

� � � � UD wpop,,z r, y, h W/p t3� (xiv)

30. From this we have the functional form of the real wage equation as: �

log (W/p) = �o + �1l + �2 log Y + �3r + �3 log WPOP + �5 UD (xv)

3. Competitiveness 31. The real exchange-rate equation is derived from the marginal cost-pricing equation for the nonagricultur sector. This links the real exchange rate to real wages and the cost of energy. As noted earlier, the long-run production function for the two sectors can be represented in the form of a CobbDouglas function, hence for the economy as whole, a Cobb Douglas production function with constant returns to scale is assumed: �

Y = � �� 1ML K 1 - � E� (xvi) �

Where, K is capital, L is labor and M is imported raw materials and E is energy. 32. Input cost C for a production unit can be represented as: �

C = Wn(1 + Tl)L + (PF/S)M + PeE (xvii)

Where, PF is foreign prices, S is nominal exchange rate and Pe is energy price index and Tl is tax on labor. 33. If the production unit maximizes profits with respect to labor, imported materials and energy, output for the unit as a function of capital, nominal wages, and energy prices can be derived (Rastogi [1989]). Further, it is assumed that the function holds at the economy level as well. In log-linear form price of output, then, would be: � Log P = �o + �1 log(W(1+Tl)) + (1 – �1) log (PF/S) + �2 log Pe + �3 log K + �4 log Y (xviii) �

Where, �3 = �4 34. We can rewrite this equation to estimate exchange rate as: � Log (PF/S) = �o + �1 log(W(1+Tl)) +�2 log P + �3 log Pe + �4 log K + �5 log Y (xix)

Where, P4 = P 5

4. Aggregate demand 35. Private final consumption expenditure accounts for about three-fifths of GDP at market prices. In this model, the private sector includes households and firms; this fits well with the Indian economy


where between 45% and 50% of total income in the economy originates in what Laumas (1990) describes a household-firm enterprises. From portfolio theory (Matthews and Minford [1980] and Rastogi [1989]) the consumption equation and the demand for the stock of physical assets can be specified as: �

� 1-tClog4log3trlog tC log 21 �� w (xx)

�5. Money market

36. The stock of real balances for the economy as a whole can be specified, using portfolio theory, as follows: � � � 1log5log4log R log 3s21 �� mwym/w (xxi)

37. To obtain an estimate of the extra risk premium generated by rising debt, the interest differential is regressed against the ratio of foreign debt to GNP. The country’s operative real foreign interest rate, then, would be:

�

� �� yfbfsrfr / (xxii)

Where bf is foreign debt valued in domestic currency and deflated by GDP deflator.

B. External Trade, Government Budget Constraint, and External Debt 38. India has grown more open to trade in the last two decades. The volume of imports is assumed to be a function of the real exchange rate and domestic output which is conventional specification for an import function (Goldstein and Khan, 1985). Historically, India has followed a policy of import controls and these have been increased or released depending on the foreign reserves position. Hence, to capture the imposition of import control and foreign exchange, rationing the lagged reserve-import ratio is taken. The functional form of the import equation can be written as:

� � � �� 1/import , import log �� Rr, yxrf (xxiii) 39. For exports, too, it is assumed that these are a function of the real exchange rate and world income, producing the following equation: �

� � � �wtrdrxrf , export log � (xxiii) 40. Now, the national account identity can be written as:

Y = c + �g + �g-1 + eg + xvol (xiv)

Where xvol = current balance on goods and services.


41. Growth in per capita income is driven by (i) growth in labor productivity (what the average worker produces); and (ii) growth in working age population (fewer the people who are in the dependent age group in the population, greater the output). C. Portfolio Selection 42. We examine the balance sheets of the public sector, the Central Bank, the non-bank commercial sector, the banking sector, and the overseas sector. We have excluded physical assets from the balance sheets as the aim of the study is to examine the behavior of the financial assets and liabilities of the non-bank commercial and the banking sectors. We shall treat the assets and liabilities of the public sector (including the Central Bank but excluding the public sector banks) as exogenous. Hence, the size of public sector borrowing requirements shall determine the quantity of the monetary base and bonds available to the other two sectors.

Source: Authors.

Table 1: Balance Sheets

1. The Public Sector

Liabilities Assets Net overseas assets NOSPS Bank deposits at

Central Bank Banks

DCB DPS

Foreign currency borrowing from banks

BB$ Foreign currency bank deposits

DG$

Government debt (B+BB+BSCB+BOS) Net worth NWPS

2. Central Bank

Liabilities Assets Notes and coins Foreign currency reserve RES Held by banks NCB Holding of public sector debt BSCB Held by NBPS NC Loan to the banking sector CL Banks’ deposits (Normal) DB Banks’ deposits (special) SPECD Public sector deposits DGCB Net worth NWCB

3. Overseas Sector Relative to India

Liabilities Assets Net overseas assets: Public sector debt BOS NBPS F$ Rs. bank deposits DOS

BF- sknaB Public Sector -NOSPS Central Bank IR Net worth NWOS

continued on next page


4. Non-Bank Commercial Sector

Liabilities Assets Bank loans Notes and coins NC Rupees L Sight deposits DS Foreign currency L$ Time deposits DT Net Fin. wealth W Foreign currency deposits D$ Net overseas assets F$ Public sector debt B

5. Banking Sector

Liabilities Assets Commercial sector: Rs. lending to commercial sector L Sight deposits DS Overseas sector LOS Time deposits DT Foreign currency lending to: Public sector deposits DPS NBPS L$ Overseas Rs. deposits DOS Public sector BB$ Non-deposit liabilities NDL Public sector debt BB Loans from the Central Bank CL Monetary base (NCB+BD) HB Special deposits SPECD Foreign currency deposit: NBPS D$ Public sector DG$ Net overseas foreign liabilities FLB Net worth NWB

43. Assuming that the net worth of the banking sector is zero (NWB=0), the following identities derived from the balance sheets would hold: �Non-banks commercial sector: L + L$ + W = NC + DS + DT + D$ + F$ + B (A) Banking sector: DS + DT + DPS + DOS + FLB + NDL + D$ + DG$ + CL = L + LOS + BB + HB + SPECD + L$ + BB$ (B) 44. These identities can be further simplified. First, we aggregate sector wise overseas assets/liabilities and foreign currency deposits/lending with domestic banks. This assumes that these two categories of assets/liabilities are close substitutes as both are subject to revaluation following exchange rate changes. Second, since our aim is to explain the behavior of the banking and non-banking commercial sector, we can treat public sector deposit with the banks (DPS) as exogenous. Similarly, Central Bank lending to the banks (CL) and rupee deposits (DOS) from overseas held with the banks may also be treated as exogenous as they are outside the immediate control of banks. Therefore, we combine these items with the banks’ non-deposit liabilities (NDL) to produce a new exogenous variable – other liabilities (OL) i.e. OL = DPS + CL + DOS + ND (C)

We can rewrite (A) and (B) as –

Table 1 continued

Source: Authors.


Non-bank commercial sector: L + W = NC + DS + DT + B + F (D)

Where F = F$ + D$ + L$ Banking sector: DS + DT + OL = L + LOS + BB + HB + SPECD + FB (E)

Where OL = DPS + DOS + NDL + CL And FB = - FLB – D$ - DG $ + L$ + BB$

45. The estimation of the portfolio behavior of the non-bank commercial sector and the banks in this study implies the estimation of the variables contained in equation (D) and (E). IV. THE MODEL ESTIMATION 46. The model has 10 behavioral equations, 6 identities, and 17 exogenous variables. All the behavioral equations were first estimated using the ordinary least square method. An estimated equation was tested for autocorrelation and partial autocorrelation (Q-Stat), normal distribution of residuals (graph and normal distribution statistics), serial correlation (Breusch-Godfrey Serial Correlation LM Test), Heteroskedasticity (Breusch-Pagan-Godfrey Test), and coefficient stability for a break in 1993 (Chow Test). A graphical presentation provides a visual check of the model estimation and residuals. The operational manual for the macro model is provided in Appendix 1 and the diagnostic test results are provided in Appendix 3.

Source: Authors’ estimates.

47. Both bank credit and bank deposits show robust simulated values. A. Three-Stage Least Squares System Estimation

Table 2: System of 10 Equations Instruments

log(rain_mm), nmm_rate1, wnrl, log(wt_2004), log(rcorp_tax), log(wpop), log(rconsu(-1)), log(rgoods(-1)), log(rm1(-1)), LOG(RIMPORT(-1)), LOG(REXPORT(-1)), LOG(REER(-1)), LOG(RW0405(-1)), LOG(EMPLOYMENT(-1)). Consumption = f(GDP at factor cost, lagged values) IPrivate sector investment = f(stock of financial assets, lagged variables, opportunity cost of investment) IINarrow Money = f(GDP at factor cost, money market rate, lagged values); IIIImports = f(GDP at factor cost, REER, world price, lagged values); IVExports = f(world trade volume, world prices, lagged values); VREER = f(real wages, corporate tax rate, lagged variables); VIReal wages = f(employment, percentage of working population, lagged values); VIIBank Credit = f(bank deposits, GDP at factor cost, lagged values, 5 year govt. securities); IXBank Deposits = f(GDP at factor cost, employment, lagged values); X


Table 3: System Estimation Result

Coefficient Std. Error t-Statistic Prob. K(1) 1.001742 0.064338 15.56998 0.0000 K(2) 0.862706 0.006886 125.2878 0.0000 K(3) 1.034801 0.112276 9.216574 0.0000 K(4) -0.273152 0.108673 -2.513516 0.0122 C(1) 0.409146 0.132689 3.083508 0.0021 C(2) 0.130226 0.051724 2.517702 0.0121 C(3) 0.850614 0.058746 14.47962 0.0000 C(4) -0.004140 0.001765 -2.345725 0.0193 C(5) 0.984887 0.121509 8.105447 0.0000 C(6) -0.193141 0.063939 -3.020734 0.0026 M(1) -0.824792 0.198773 -4.149411 0.0000 M(2) 0.280060 0.071766 3.902389 0.0001 M(3) -0.005921 0.002039 -2.903316 0.0038 M(4) 0.776762 0.063676 12.19874 0.0000 I(1) 0.047969 0.652087 0.073563 0.9414 I(2) 0.346616 0.095025 3.647618 0.0003 I(3) -0.291903 0.085742 -3.404441 0.0007 I(4) -0.008440 0.006512 -1.296114 0.1955 I(5) 0.753737 0.065544 11.49963 0.0000 T(1) -0.342048 0.077542 -4.411115 0.0000 T(2) 0.180536 0.043248 4.174416 0.0000 T(3) 0.901269 0.032467 27.75919 0.0000 T(4) -0.017063 0.005952 -2.866611 0.0043 X(1) 0.483214 0.219840 2.198027 0.0284 X(2) -0.069389 0.026232 -2.645195 0.0084 X(3) 0.074645 0.032430 2.301732 0.0217 X(4) 0.858661 0.049850 17.22506 0.0000 W(1) -2.610681 1.475277 -1.769621 0.0773 W(2) -0.146763 0.200860 -0.730673 0.4653 W(3) 0.580949 0.377263 1.539906 0.1241 W(4) 0.882586 0.072362 12.19674 0.0000 P(1) -2.223398 0.661014 -3.363617 0.0008 P(2) -0.121927 0.048813 -2.497847 0.0128 P(3) -0.127046 0.036650 -3.466457 0.0006 P(4) 0.712769 0.217357 3.279250 0.0011 P(5) 0.874018 0.039101 22.35303 0.0000 C(7) 0.025297 1.004513 0.025183 0.9799 C(8) 0.047968 0.066069 0.726025 0.4681 C(9) 0.004263 0.131581 0.032395 0.9742 C(10) 0.967210 0.068458 14.12862 0.0000 C(11) -0.008512 0.006232 -1.365970 0.1725 C(12) -1.572550 0.908229 -1.731448 0.0839 C(13) 0.190360 0.112997 1.684647 0.0926 C(14) 0.080475 0.053884 1.493491 0.1359 C(15) 0.890647 0.066848 13.32347 0.0000 Determinant residual covariance 5.83E-29

Source: Authors' estimates.


Figure 2: System Residuals

Table 4: System with the Estimated Coefficients LOG(RCONSU) = 1.00174242277 + 0.862705690016*LOG(RGDP_FC)+ [AR(1)=1.03480140267, AR(2)=-0.273151584456]



LOG(RGOODS) = 0.927920622737 + 0.800566759545*LOG(RTHETA) + 0.0727016613911*LOG(RGOODS(-1)) + 0.792077745068*NRL + [AR(1)=1.01513478344,AR(2)=1.01513478344] LOG(RM1) = -0.824792075062 + 0.280060047772*LOG(RGDP_FC) - 0.00592079744669*NMM_RATE1 + 0.776762477268*LOG(RM1(-1)) LOG(RIMPORT) = 0.0479691856314 + 0.346615646176*LOG(RGDP_FC) - 0.291902888313*LOG(REER) - 0.00844014923841*WNRL + 0.753736889042*LOG(RIMPORT(-1)) LOG(REXPORT) = -0.342047610455 + 0.180535767166*LOG(WT_2004) + 0.901268992167*LOG(REXPORT(-1)) - 0.0170628780532*WNRL LOG(REER) = 0.483214032229 - 0.0693894927289*LOG(RW0405) + 0.0746445617832*LOG(RCORP_TAX) + 0.85866131019*LOG(REER(-1)) LOG(RW0405) = -2.61068113859 - 0.146762994249*LOG(EMPLOYMENT) + 0.580949274787*LOG(WPOP) + 0.882586392487*LOG(RW0405(-1)) LOG(EMPLOYMENT) = -2.22339791769 - 0.121926530038*LOG(TU) - 0.127046154964*LOG(RW0405) + 0.712769368638*LOG(WPOP) + 0.874018316625*LOG(EMPLOYMENT(-1)) LOG(RBANK_CRED) = 0.025297 + 0.047968*LOG(RBANK_DEPO) + 0.004263*LOG(RGDP_FC) + 0.967210*LOG(RBANK_CRED(-1)) -0.008512*NRS LOG(RBANK_DEPO) = -1.572550 + 0.190360*LOG(RGDP_FC) + 0.080475*LOG(EMPLOYMENT) + 0.0890647*LOG(RBANK_DEPO(-1))


Table 5: System Variables 21

RCONSU Endog Eq1 Source: T003A RGFCF-Pvt RGOODS Endog Eq2 Accumulated using Private Sector GDCF in Rs Billion in 2004-5 prices RM1 Endog Eq3 RM1=NM1*100/WPI RIMPORT Endog Eq4 T003A REXPORT Endog Eq5 Exports of Goods and Services Rs bn in 2004-5 prices REER Endog Eq6 T149 and Indiastat.com RW0405 Endog Eq7 Note 2004-5=100 EMPLOYMENT Endog Eq8 Source:Indiastat.com and Ess RBANK_CRED Endog Eq9 RBANK_DEPO Endog Eq10 RGDP_FC Endog Eq11 RGDP-FC RWEALTH Endog Eq12 Rwealth=Rtheta+Rgoods RTHETA Endog Eq13 Accumulated using Rhous-Fin Assets REG Endog Eq14 XVOL Endog Eq15 xvol=rexport-rimport INFL Endog Eq16 NMM_RATE1 Exog Exog T74--1950-51 to 1970-71 from IFS database NRL Exog Exog T74-Took first no. as 1 yr yield NRS Exog Exog Annual (Gross) Redemption Yield of Government of India

Securities-5-10 year R_CG_INTEREST PAYMENT Exog Exog R-CG-interest payment RAFC04_05 Exog Exog RCORP_TAX Exog Exog Rcorp Tax RDISCRE Exog Exog T003A RGFCF Exog Exog Gross fixed capital formation Rs Bn in 2004-5 prices RGFCF_DSTOCK Exog Exog RGFCF-Dstock RGFCF_PVT_SEC Exog Exog RGFCF-Pvt Sec RGOVT_CONS Exog Exog Rgovt cons RTAX_REV Exog Exog Rtax Rev TU Exog Exog Indiastat and ES WNRL Exog Exog Source – IFS WPI04_05 Exog Exog WPI04-05 WPOP Exog Exog calculated using population and decadel growth in old age population. PivoteWT_2004



Exog Exog WT-2004

Table 6: Model with System Equations and Identities rgdp_fc = rconsu + reg + rgfcf_pvt_sec + rexport - rimport + rdiscre + rgfcf_dstock - rafc04_05

rwealth = rgoods + rtheta

rtheta = reg - rTAX_rev + R_cg_interest_payment + xvol

reg = rgovt_cons + rgfcf

xvol = rexport - rimport

infl = (d(wpi04_05)) * 100

21 A description of data sources is provided in Appendix 4.


48. The 3SLS system estimates are used in the model along with the identities to solve the model. First Static-Deterministic Simulation of the model is carried out followed by the dynamic deterministic simulation for the baseline forecast.22 The forecast of the exogenous variables is provided in Appendix 5. B. Static-Deterministic Simulation of the Model 49. This model is used to carry out Dynamic Deterministic simulation over the 2013 to 2018 period. The GDP, REER, investment, bank credit and bank deposits show a smooth forecast over the simulation period of 2013 to 2018. In the following graphs the vertical line at 2013 separates historical simulation of the model and forecast of the model given the trajectory of exogenous variables.


Figure 3: Graph of Simulated Endogenous Variables

22 In the Static-Deterministic Simulation actual lagged value of the endogenous variable is taken as lagged variables. The

simulation rests show a very good fit. Later, Dynamic-Deterministic Simulation of the model is carried out and all the ** of actual values with the baseline simulation values are plotted as a graph. In the Dynamic-Deterministic Simulation model solved value of the endogenous variable is taken as lagged variables.


C. Dynamic-Deterministic Simulation of the Model

The following table gives baseline data of these variables in from 2011 to 2018.



Table 7: Baseline Data from 2011 to 2018 (in Rs billion)

Table 8: Annual Growth Rate from 2011 to 2018 (Baseline Forecast)

Figure 4: Dynamic-Deterministic Simulations

The following table gives the annual growth rate of these variables from 2011 to 2018 (baseline forecast)


(a) Simulation 1

50. Add factor on the Real Effective Exchange Rate was changed to simulate fall of 10% of real effective exchange rate.23 The model was simulated over the period 2013 to 2018. The following graphs show the difference between the baseline and the simulation when the REER depreciates by 10.86% in 2013. While it does not appreciate in the following years, the depreciation is less precipitous and by 2018 REER depreciates by only 5.4%. The following graphs show that consumption falls gradually but continues to fall. Investments grow a gradually, due to the fact that fall in REER is achieved through fall in real wages in the model. As real wages fall, investment demand goes up in the economy. Exports remain the same but imports rise. However, overall GDP contracts gradually over the simulation period. Fall in GDP leads to fall in bank deposits and a marginal reduction in the overall demand of bank credit.


Figure 5: Simulation 1 REER

23 When a model with all the behavioral equations along with identities is used for simulation purposes, all the projected

variables may not match with the last actual value of the endogenous variable because of residuals between the fitted value of the behavioral model and the actual value of the variable. This may lead to sharp dips in variables at the beginning of the forecast period. One way to deal with this problem is to model the path of the residual. More often than not behavioral equation intercept is changed to smooth the sharp dips. Using the add factor, we can specify any path we choose for the residuals of the equation. Add factors have been used in the consumption, goods and real effective exchange rate equations which showed sharp movement at the beginning of the forecast period.


The following are the variables 2011 to 2018 period (

Table 9: 2011 to 2018 Variables Forecast after 10% REER depreciation (in Rs billion)

Table 10: Annual Growth Rate after 10% fall in REER

forecast after the 10% REER depreciation) In Rs billion

The following are the annual growth rates of



the variables after the 10% fall in REER.

(b) Simulation II 51. This simulation is carried out to demonstrate the impact of a slowdown in growth due to a compression in investment and consumption. The simulation is carried out by reducing government expenditure by 1% compared to the base line simulation (2004-5 prices and not in nominal terms). It is found that consumption falls by more than 3% and investment by more than 12%. REER remain at the same level as the baseline. Further, exports growth remains intact but imports fall gradually with GDP. Bank deposits gradually reduce compared with the baseline but bank credit demand remains robust.


52. The following table provides the forecast followed by annual



growth rate of the variables.24

24 Real consumption and investments fall in 2013 and investments fall in 2014 as well in absolute terms in 2004-05 prices.

Figure 6: Simulation 2

Table 11: Forecasts and Annual Growth Rate Variables


V. FINANCIAL MODELING OF INDIAN BANKS A. Disclosure and Reporting Requirements 53. As indicated in the introduction, the next step is to determine the bank capital requirements given the credit and deposit forecasts, and the infrastructure lending needs of the economy. The modelling of bank capital requirements is also conducted in the context of Basel III norms. Banks are required to disclose the quantum of tier 1 and tier 2 capital. Annual reports to be submitted to RBI indicate capital funds, conversion of o -balance sheet/non-funded exposures, calculation of risk-weighted assets, and calculations of capital to risk assets ratio. The RBI set a target date of 31 March 2008 for the implementation of the Basel II Framework for foreign banks in India and Indian banks with foreign operations, and a deadline of 31 March 2009 for migration to Basel II approaches by all other scheduled commercial banks, and a target of achieving the tier 1 capital ratio of not later on 31 March 2010 both on solo and consolidated basis. The risk weighting is calculated by using a standardized or internal-ratings based approach. There are some key di erences between global Basel II accord and RBI norms.

Original Basel Accord RBI GuidelinesThe total capital ratio must be no lower than 8%

Banks are required to maintain a minimum capital to risk-weighted assets ratio (CRAR) of 9% on an ongoing basis

Banks have been permitted to adopt the standardized method, internal rating based or advanced measurement approach

Banks mandated to use standardized approach for credit risk and basic indicator approach for operational risk. Banks to make a road map for migration to advanced approaches only after obtaining specific approval of RBI

Claims on sovereigns to be risk weighted from 0% to 150% depending upon the credit assessments – AAA to B-

Exposures to domestic sovereigns

Source: Authors.

(central and states) rated at 0%Exposure to banks outside India—20% to 150% depending upon the credit assessment of the banks

Lending against fully secured mortgages on residential property will be risk weighted at 35%

Lending against fully secured mortgages if the loan to value ratio (LTV) is not more than 75%, on residential property will be risk weighted at 75%, except where loan value is below Rs.30 lacs which is risk weighted at 50%

54. Instruments under this accord. RBI allowed banks to augment their capital funds by issue of following: 1. For Tier 1 Capital (w.e.f. 25, 2006):

(i) innovative perpetual debt instruments (ii) perpetual non-cumulative preference shares

2. For Upper Tier 2 Capital (w.e.f. Oct 2007):

(i) perpetual cumulative preference shares (PCPS) (ii) redeemable non-cumulative preference shares (iii) redeemable cumulative preference shares (RCPS)

B. Risk Management 55. Risk management in banks implies fulfilling Basel III capital adequacy as laid down by RBI. At present, all banks satisfy Basel II norms and preparing themselves to meet Basel III norms. For calculation of risk weighted assets to maintain capital, di ent approaches have been suggested under Basel II. These approaches are:


Credit Risk ++ standard approach, internal rating based approach, and advance approach Market Risk ++ standard approach (comprising maturity method and duration method), internal risk

based approach Operational Risk ++ basic indicator approach, standard approach, advance measurement approach

The approaches marked ++, have already been implemented w.e.f. 31.3.08 and 31.3.09.

Table 13: RBI Plan for Shifting to Advance Approaches Approach Earliest date ++ Likely date +++ Internal models approach for market risk 1 April 2010 31 March 2011 Standardized approach for operational risk, advanced measurement approach for operational risk 1 April 2010 30 September 2010 Internal ratings-based approaches for credit risk (foundation as wellas advanced) 1 April 2012 31 March 2014

++Earliest date to make application by banks to RBI +++ Likely date of approval by the RBI

Source: RBI. C. Impact of Basel III on Indian Banks 56. On 30 June 2010, the aggregate capital to risk-weighted assets ratio of the Indian banking system stood at 13.4%, of which tier 1 capital constituted 9.3%. As such, RBI does not expect Indian banking system to be significantly stretched in meeting the proposed new capital rules, both in terms of the overall capital requirement and the quality of capital. There may be some negative impact arising from shifting some deductions from tier 1 and tier 2 capitals to common equity. Indian banks are moderately leveraged. The RBI has indicated that the government will have to contribute to help public sector meet their capital requirements and also maintain their 51% equity share. Following are the salient features to meet Basel III norms by Indian banks. 57. Capital adequacy. For Indian banks, it is easier to make the transition to a stricter capital requirement regime than some of their international counterparts since the regulatory norms set by RBI on capital adequacy are already more stringent. Besides, most Indian banks have historically maintained their core and overall capital well in excess of the regulatory minimum. According to a CRISIL estimate, the average equity capital ratio and overall capital adequacy ratio of rated banks in India stand well above 9% and 14%, respectively. As for federal bank, its tier 1 capital is 15.86% and its capital adequacy stands at 16.64% as on 31 March 2012, significantly higher than the stipulated norms. 58. Cost of lending. Stricter capital requirements with changes in the structure of tier 1 and tier 2 capitals generally result in lower return on equity (ROE). On the flip side, as capital costs increase, loans tend to be expensive. In order to this, banks would have to take the route of reducing deposit interest and go in for new non-interest income streams. Yet, Basel III carries the message that Indian banks will have to start finding ways to preserve capital and use it more productively as minimum capital requirements will have to be met by 31 March 2017. 59. Leverage. RBI has set the leverage ratio at 4.5% (3% under Basel III).The ratio is introduced by Basel III to regulate banks having huge trading book and balance sheet derivative positions. In India, however, in most of our banks, the derivative activities are not very large so as to arrange enhanced cover for counterparty credit risk. Hence, the pressure on banks should be moderate.


60. Liquidity norms. Indian banks conform to two liquidity buffers namely SLR and CRR. The liquidity coverage ratio (LCR) under Basel III requires banks to hold enough unencumbered liquid assets to cover expected net outflows during a 30-day stress period. In India, the burden from LCR stipulation will depend on how much of CRR and SLR can be offset against LCR. 61. Countercyclical buffer. Economic activity moves in cycles and banks are inherently pro-cyclic. During upswings, banks end up in excessive lending and unchecked risk build-up, which carry the seeds of a disastrous downturn. The regulation to create additional capital buffers to lend further would act as a break on unbridled bank-lending. This check will counter or smoothen wild swings in business cycles. India has witnessed moderate cycles. Yet, for countercyclical measures to be effective, our banking system has to improve its capability to sense and predict the business cycle at sectoral and systemic levels and to use tools like “credit to GDP ratio” to calibrate the level of countercyclical buffer. 62. Apart from meeting the Basel III norms of capital adequacy, the bank model is to provide tier 1 capital requirement, if banks are to fulfill the planning commission infrastructure lending program. Details of the financial modeling of individual banks are explained in Appendix 2.25 Based on this we provide in the table below the CAR requirements of a few large public sector banks both with respect to the baseline and the simulation results.

Table 14: CAR requirements for Large Public Sector Banks (baseline and simulation results)

March 2014 March 2015 March 2016 March 2017 March 2018Industrial Development Bank of India Base line CAR (% above Basel III requirements) 1.36 0.77 0.90 1.20 1.64CAR (% below BASEL III requirements after 10% REER depreciation)

1.06 -0.28 -1.27 -2.41 -3.67

CAR (% below BASEL III requirements after GDP slowdown)

1.06 -0.27 -1.28 -2.46 -3.80

State Bank of India Base line CAR (% above Basel III requirements) 2.18 1.62 1.58 1.50 1.38CAR (% below BASEL III requirements after 10% REER depreciation)

1.82 0.46 -0.72 -2.17 -3.81


1.82 0.47 -0.72 -2.21 -3.49

Bank of Baroda Base line CAR (% above Basel III requirements) 3.04 2.47 2.49 2.27 1.94CAR (% below BASEL III requirements after 10% REER depreciation)

2.64 1.07 -0.42 -2.45 -4.80


2.64 1.08 -0.43 -2.52 -4.98

Punjab National Bank Base line CAR (% above Basel III requirements)

2.43 2.40 3.00 3.65 4.32

CAR (% below BASEL III requirements after 10% REER depreciation)

2.08 1.02 0.03 -1.36 -3.06


Source: Authors' Estimates.

2.08 1.03 0.02 -1.43 -3.25

25 We have financial model of 20 banks which cover approximately 75% of credit and deposits of Indian banks.


VI. CONCLUSION AND KEY RESULTS 63. The aim of this study is to determine the capital requirements of Indian banks in the context of the Indian economy’s infrastructure lending requirements. In doing so the paper developed a model of the Indian economy to forecast key macroeconomic variables and of banks. The study is a partial equilibrium model as it assumes the real sector of the economy as given. The model deals with the determination of money supply and its interaction with bank credit, exchange rate, monetary policy instruments, and interest rates. The effect of the foreign exchange market is also studied. The model also takes into account the Indian institutional framework through policy or institutional variables. 64. In the base case overall bank credit and bank deposits were projected at a CAGR of 14.3% and 13.1% respectively between FY2008 and FY 2012 at current prices. Reserve and surpluses also were growing in double digits and hence banks had no difficulty in meeting tier 1 capital norms. In the simulation where GDP growth rate reduces to ~4% per annum, bank credit and bank deposits in 2004-05 prices now grow only 9.8% and 11.5%. But due to ~10% rate of inflation, the CAGR for bank credit and bank deposits between FY 2013 and FY 2018 is 21.5% and 23.1% respectively in current prices.26 Reserve and surpluses of the banks do not grow at the same pace. By FY2017 and FY2018, reserve and surpluses reduce. Therefore, a large number of banks show shortfall in Tier1 capital. Note that minimum Tier 1 capital requirement prescribed by the RBI in 2007 and 2008 is 7% compared to 6% in FY 2013. 65. In the simulation where REER depreciates by 10% nominal bank credit and deposits grow by 16.7% and 17.7%. The tier 1 capital requirement in this scenario is even more than the previous scenario as reserve and surpluses reduce very sharply and deposits growth fall due to fall in GDP growth. Different banks show different shortfall in Tier 1 capital as dividend rates are different for different banks. 66. In both the simulations bank credit and deposits growths are higher than the base case scenario because bank credit and deposits are at current prices i.e. inflation is added to real growth of credit and deposits. Comparative to the base case deposits growth rate slows down as growth rate of GDP falls. In both simulations, most of the public sector banks show a shortfall in Tier 1 capital from the prescribed Tier 1 capital norm of the RBI. 27 Reason for this dichotomy comes from the fact that the banking sector model, based on 20 largest banks in India, critically depends on net interest margin (NIM) of individual banks and risk weighted assets (RWA) in the banks. NIM varies in the range of 120 to 170 basis points and RWA varies from 71% to 89%. 28 Both NIM and RWA are kept constant at the FY 2013 level as given in individual bank’s balance sheet. Private sector banks have NIM and RWA at the higher end of the range. Consequently, after transfer to statutory reserves (CRR and SLR) and payment as dividend to shareholders the retained earnings starts depleting very quickly among public sector banks.29 We have assumed that dividend rate is kept constant in the two alternative simulations. In case of private sector banks, due to higher NIM and low dividend, retained earnings still remain healthy and they are able to meet their Tier 1 capital requirement although size of the cushion compared to the base case is lower. 26 Banking sector credit and deposit rates forecast in the macro model are in 2004-05 prices and these are in current prices

in the banking sector model. Hence, the macro model forecast is scaled up by using the model predicted rate of inflation (WPI).

27 The three private sector banks namely, Axis Bank, Industrial Credit and Investment Corporation of India (ICICI) Bank and the Housing Development Finance Corporation (HDFC) Bank have more than adequate Tier 1 capital.

28 ICICI Bank has 106% as it reported RWA on consolidated basis and Tier 1 capital as a standalone entity. 29 Note that government being main shareholder, main beneficiary of dividend payout is the government.

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APPENDIX 1 Operational manual of the macroeconomic model The EView7 is an integrated estimation, solution and forecasting software for econometric modeling. This software is used to estimate, solve and forecast macroeconomy model using time series from 1951-2012. The model data file (macro model-29june2013.WF1) is read by the EView7 package. The model has 10 behavioral equations, 6 identities and 17 exogenous variables. All the behavioral equations were first estimated using the ordinary least square method. An estimated equation was tested for autocorrelation and partial autocorrelation (Q-Stat), normal distribution of residuals (graph and normal distribution statistics), serial correlation (Breusch-Godfrey Serial Correlation LM Test), Heteroskedasticity (Breusch-Pagan-Godfrey Test) and coefficient stability for a break in 1993 (Chow Test). A graphical presentation provides a visual check of the model estimation and residuals. The system estimation of the 10 behavioral equations was carried out using the Three-Stage Least Squares method which takes the estimates of the least square equations as the starting point. The Three-Stage Least Squares estimated coefficient reported along with statistical properties of each of the equation. The residuals of each of the equations were put in a graph for a visual test. The 3SLS system estimates are used in the model along with the identities to solve the model. First Static-Deterministic Simulation of the model is carried out actual values of variables – banking sector credit in the economy, banking sector deposit in the economy, private sector financial consumption, narrow money, organized sector employment, wages in PSUs, exports of goods and services, imports of goods and services, trade balances and trade weighted real effective exchange rate -- with the baseline simulation values are plotted as a graph. In the Static-Deterministic Simulation actual lagged value of the endogenous variable is taken as lagged variables. The simulation results show a very good fit. We used EViews 7 to write forecast of bank credit and bank deposits from 2013 -18 in nominal terms. A small excel file is created by the EViews 7 having bank credit and bank deposits in billions of rupees. While forecast is made of all the endogenous variables, only five year forecast of overall bank credit and deposit in the economy is generated to be used by the banking sector financial model. An excel file is created by the Eviews 7 (bankCD.xls is attached which is read by excel software). WPI forecast of the model was used to convert bank credit and bank deposit in nominal figures. Data from bankCD.xls is to be copied (cells B2:G3 from bankcd worksheet of bankCD.xls file) and pasted in the banking sector model (‘Banking Sector Model-July20.xlsx’) in the Inputs worksheet of the model in cells L3:P4. Rest of calculations are taken care by the banking sector model itself as it is fully linked and output is given in the ‘CAR Req’ worksheet of the model in terms of %deficit/surplus compared to the Basle III norms. Exact number of CAR in Crore of Rs of individual banks is in individual bank’s worksheet. Note that in the CAR Req worksheet if tier 1 capital of any bank is less than 0.5% it turns into redcolor. �

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APPENDIX 2 Operational manual of the Financial Model of Indian Banks Note that numbers in ‘Inputs’ and ‘CAR Req’ are in billions of rupees and bank model numbers are in crore of rupees (One billion = 100 crores) Model Structure The model has the following 22 worksheets.

1. Input Worksheet The input worksheet has main input variables which need to be changed in the model. The worksheet has 44 blocks. The first block has Bank_Credit _Forecast, Bank_Deposit_Forecast and Equity dividend tax. The two forecast variables are from the macroeconomic forecast of the macroeconomic model. These variables forecast bank credit and deposit at the economy wide level which are determined within the econometric model. The equity dividend tax is an exogenous variable and set by government fiscal policies. Next 20 blocks are of 20 banks. Each block has individual bank’s credit, deposit, net interest margin and risk weighted assets data and five year forecast. The five year credit and deposit forecast is on the basis of assumption that each bank maintains its credit and deposit share.

The net interest margin is a crucial variable which drives the profitability of the bank and kept constant. The Risk weighted assets of a bank is kept constant. These four variables are used to forecast bank’s balance sheet, profit and loss statement, capital structure and risk assets.

Next block is control block to check total proportion of the 20 banks credit and deposit in the whole economy and credit to deposit ratio of all the 20 banks. Next block has the five year plan data and investment planned for the next five years.

Next block has data on statutory capital adequacy ratios as prescribed by RBI for implementing Basle III norms in the banking sector.

Next 20 blocks give individual banks fund and non-fund based exposure to infrastructure and forecast these variables for the next five years. Total exposure of individual banks on proportionate basis as a proportion to planned infrastructure expenditure of the private sector as suggested by the XIII five year plan are projected for the next five years.


2. CAR Req Worksheet

The CAR Req worksheet has 20 blocks of individual banks and has deficit or surplus of tier 1 capital three scenarios namely base case, infrastructure exposure as proportion to their total exposure as of now and infrastructure exposure as proportion to XII five year private sector plan. Only tier 1 capital deficit and surplus is calculated.

3. Individual Banks Worksheets (20) Allahbad, Andhra, Axis, BOB, BOI, CAN, CBI, CORP, HDFC, ICICI, IDBI, Indian, IOB, OBC, PNB, SBH, SBI, SYN, UBI and UCO are 20 worksheets in this model having 20 banks model. All models have actual data collected from their annual report and have five years of forecast. Blue background is the forecast of variables. Red numerals are critical numerals which drive the model.

All models are standardized and have the following four blocks.

a. Sources and Application of funds (Balance Sheet Block) b. Profit and Loss Block c. Risk Weighted Assets Block d. Capital Adequacy Block

Though capital adequacy norms prescribed by the RBI are followed by all the banks but there is no uniformity in reporting the numbers. We have concentrated on calculation of tier 1 capital for the banks as our study of balance sheet of the banks suggest that raising tier 2 capital for the banks is not a problem. Various instruments under the head of Innovative Perpetual Debt Instruments are used by the banks to meet tier 1 capital requirements.

The purpose of the Banking Sector Model is to be used along with the macroeconomic model forecast of bank credit and bank deposits and calculate tier 1 capital shortfall for banks, if any, and requirements of tier 1 capital if credit exposure to infrastructure sector is in line with business as usual scenario and as proportion to the XII Five Year Plan. The following assumptions are used to project individual bank’s balance sheet. To project the profit & loss account and balance sheet of the bank for next five years, various assumptions are made. Block-by-block we provide general rationale for the projects. Based on the projection, expected Capital Adequacy Ratio of the bank has been calculated. Sources and Applications of Funds (Balance Sheet) 1. Deposit of a bank is taken as proportion of the total deposits forecasted in the macromodel and

split in the same proportions as the base year contribution of the bank in total deposits. 2. Credit disbursement of a bank is taken as proportion of the total credit forecasted in the macro

model and split in the same proportions as the base year contribution of the bank in total credit. 3. Equity capital is kept constant.


4. Share holders’ reserves are growing as per the P&L calculation of the model. 5. Borrowings are two year moving average including borrowing from other banks and institutions. 6. Cash and balances with RBI is three year moving average. 7. Balances with banks and money at call are balancing item. 8. Investment is a two year moving average. 9. Fixed assets and other assets grow at the same rate as the base year for the forecast.

Profit and Loss Statement 1. Total income is projected using Net Interest Margin (NIM) constant as of the base year. The NIM

projection for each bank is made in the ‘Input’ worksheet. 2. Income earned and other income projections have same proportion as the base year. 3. Interest expenditure, payments and operation expenses grow in proportion of liabilities year-after-

year. 4. Other expenses growth rates are different for different banks but growing at the same rate for all

the five years. This figure is based on annual report of the bank. 5. Provision for tax grows as % of total income of the base year. 6. Transfer to statutory reserve is 25% of net transfers as per section 17 of the Banking Regulation Act. 7. Transfer to government is constant proportion of net profit. 8. Balance Carried forward to the balance sheet is (net profit – dividend tax – dividend tax). 9. Equity dividend is kept constant as of the base year.

Capital Adequacy 1. Risk weighted assets of FY2012 and FY2013 is culled out of the annual reports. Risk weighted assets

as %age interest and advances is calculated and kept constant. 2. Risk weight of infrastructure exposure is kept one as per RBI guidelines. 3. Risk weighted assets (RWA) of a bank are projected using RWA as %age of investments and

advances. Base year RWA is taken from the audited balance sheet of the bank. 4. Tier 2 capital is kept constant. Note that raising tier 2 capital is easy for a bank to raise. 5. Tier 1 capital is sum of paid up share capital, reserves and surpluses, innovative perpetual debt

instrument after deduction made for NPAs. 6. Deduction made for NPAs grow in the same proportion as increase in share capital and reserves. 7. Paid up capital and reserves come from the balance sheet data.

Infrastructure Exposure of a bank We calculate fund and non-fund based exposure of the bank in the base year. Infrastructure Exposure Sustainability We have assumed 2 situations, namely, �� Considering growth of the bank and its exposure to infrastructure sector: It is based on the bank’s

growth plans and keeping exposure to infrastructure sector constant (as in 2012) in the coming


years. Here the RWA might grow as it has been growing in the past few years or based on total assets growth rate

�� Considering 12th five year plan: It is based on expected investment in infrastructure sector in 12th five year plan and market share of the bank in financing to the sector. Here the RWA might grow a notch higher than in the above case considering the fact that the exposure in Infra (generally 100% or more of risk weightage) might be increasing as against less risky advances/investments

Possible shortfall in CAR due to higher exposure to infrastructure sector is also calculated for both the scenarios. Calculation of what is the maximum exposure the bank can have towards infrastructure sector is done so that the CAR does not fall below the requirement (CAR Req worksheet). Maximum Infrastructure Sustainability in 2nd case It is based on the calculations done for the 2nd case above. Here the model calculates the maximum exposure to the infrastructure sector that the bank can sustain without violating the Basel norms. These norms are prescribed by the RBI and given in the ‘Inputs’ worksheet. Infrastructure Exposure – Financial Modeling Steps Modeling of a bank's exposure to infrastructure is done at two levels as mentioned above: i. Based on the bank's current exposure to Infrastructure Sector and its growth projections for next 5

years ii. Based on 12th FYP and the bank's share in that, out of total private investment

Sensitivity Analysis on Amount of Capital Infusion 1. Practically it is not possible for any bank to put onus on only a particular sector (Infrastructure in

this case), so we have done a sensitivity analysis based on Capital Infusion required by the bank to meet Basel III requirements, as Core Equity Capital (CET-I) requirements are also a part of Basel III requirements.

2. So for that purpose we started increasing CET-I by 5% up to X% (Comes out to be 44.30% in case of YES Bank) till we meet the Basel III norms without any reduction in Infrastructure Exposure.

3. EViews 7 is used to write forecast of bank credit and bank deposits from 2013 -18 in nominal terms. A small excel file is created by the EViews 7 having bank credit and bank deposits in billions of rupees.

4. While forecast is made of all the endogenous variables, only five year forecast of overall bank credit and deposit in the economy is generated in an excel file by the Eviews 7 (bankCD.xls is attached which is read by excel software). WPI forecast of the model was used to convert bank credit and bank deposit in nominal figures. Data from bankCD.xls is to be copied (cells B2:G3 from banked worksheet of bankCD.xls file) and pasted in the banking sector model (‘Banking Sector Model-July20.xlsx’) in the Inputs worksheet of the model in cells L3:P4. Rest of calculations are taken care by the banking sector model itself as it is fully linked and output is given in the ‘CAR Req’ worksheet of the model in terms of %deficit/surplus compared to the Basle III norms. Exact number of CAR in Crore of Rs of individual banks is in individual bank’s worksheet. Note that in the CAR Req worksheet if tier 1 capital of any bank is less than 0.5% it turns into red color.

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APPENDIX 3 Estimation of the Consumption Model using LS method

Dependent Variable: LOG(RCONSU) Method: Least Squares Sample (adjusted): 1953 2012 Included observations: 60 after adjustments Convergence achieved after 5 iterations

Variable Coefficient Std. Error t-Statistic Prob.

C 1.012537 0.066214 15.29186 0.0000 LOG(RGDP_FC) 0.861572 0.007083 121.6349 0.0000 AR(1) 1.021776 0.120785 8.459478 0.0000 AR(2) -0.260924 0.115900 -2.251281 0.0283 R-squared 0.999765 Mean dependent var 8.945388 Adjusted R-squared 0.999753 S.D. dependent var 0.701494 S.E. of regression 0.011035 Akaike info criterion -6.111129 Sum squared resid 0.006819 Schwarz criterion -5.971506 Log likelihood 187.3339 Hannan-Quinn criter. -6.056515 F-statistic 79455.45 Durbin-Watson stat 2.098028 Prob(F-statistic) 0.000000 Inverted AR Roots .52 .50

Autocorrelation and Partial Autocorrelation Test for the Consumption Model

Sample: 1953 2012 Included observations: 60 Q-statistic probabilities adjustedfor 2 ARMA term(s)

Autocorrelation Partial Correlation AC PAC Q-Stat Prob . | . | . | . | 1 -0.050 -0.050 0.1580 . |*. | . |*. | 2 0.085 0.082 0.6190 . | . | . | . | 3 -0.002 0.006 0.6193 0.431 . | . | . | . | 4 -0.038 -0.045 0.7151 0.699

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Histogram of the residual of the Consumption Model (Normality Statistics)

Serial Correlation Test for the Consumption model Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.210761 Prob. F(4,52) 0.9313 Obs*R-squared 0.957224 Prob. Chi-Square(4) 0.9162 Test Equation: Dependent Variable: RESID Method: Least Squares Sample: 1953 2012 Included observations: 60 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic Prob. C -0.005661 0.073558 -0.076965 0.9389 LOG(RGDP_FC) 0.000555 0.007827 0.070935 0.9437 AR(1) 0.127843 0.567829 0.225143 0.8228 AR(2) -0.120768 0.283494 -0.425998 0.6719 RESID(-1) -0.176675 0.585446 -0.301779 0.7640 RESID(-2) 0.075994 0.400092 0.189941 0.8501 RESID(-3) 0.028185 0.297696 0.094678 0.9249 RESID(-4) -0.019283 0.217598 -0.088620 0.9297 R-squared 0.015954 Mean dependent var 3.11E-14 Adjusted R-squared -0.116514 S.D. dependent var 0.010751 S.E. of regression 0.011360 Akaike info criterion -5.993878 Sum squared resid 0.006711 Schwarz criterion -5.714632 Log likelihood 187.8163 Hannan-Quinn criter. -5.884649 F-statistic 0.120435 Durbin-Watson stat 2.006831 Prob(F-statistic) 0.996590

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Heteroskedasticity Test for the Consumption Model

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 1.997989 Prob. F(1,58) 0.1629

Obs*R-squared 1.998056 Prob. Chi-Square(1) 0.1575

Scaled explained SS 1.904863 Prob. Chi-Square(1) 0.1675

Test Equation:

Dependent Variable: RESID^2

Method: Least Squares

Sample: 1953 2012

Included observations: 60


C 0.000463 0.000248 1.865930 0.0671

LOG(RGDP_FC) -3.80E-05 2.69E-05 -1.413502 0.1629

R-squared 0.033301 Mean dependent var 0.000114

Adjusted R-squared 0.016634 S.D. dependent var 0.000170

S.E. of regression 0.000168 Akaike info criterion -14.51063

Sum squared resid 1.64E-06 Schwarz criterion -14.44082

Log likelihood 437.3190 Hannan-Quinn criter. -14.48333

F-statistic 1.997989 Durbin-Watson stat 2.212228

Prob(F-statistic) 0.162853

Parameter Stability Test of the Consumption Model (Chow Test at the year 1994)

Chow Breakpoint Test: 1994

Null Hypothesis: No breaks at specified breakpoints

Equation Sample: 1953 2012

F-statistic 0.483181 Prob. F(4,52) 0.7479

Log likelihood ratio 2.189621 Prob. Chi-Square(4) 0.7009

Wald Statistic 1.460005 Prob. Chi-Square(4) 0.8337

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Graph of the Consumption Model and its Residuals

Consumption Model LOG(RCONSU) = 1.01253715015 + 0.861572234474*LOG(RGDP_FC) + [AR(1)=1.02177568134,AR(2)=-0.260924296966]

Estimation of the Goods Model using LS method

Dependent Variable: LOG(RGOODS) Method: Least Squares Sample (adjusted): 1954 2012 Included observations: 59 after adjustments Convergence achieved after 11 iterations Variable Coefficient Std. Error t-Statistic Prob. C 0.415733 0.158513 2.622705 0.0114 LOG(RTHETA) 0.132094 0.062212 2.123300 0.0384 LOG(RGOODS(-1)) 0.846624 0.070536 12.00271 0.0000 NRL -0.001507 0.001272 -1.185201 0.2412 AR(1) 1.014380 0.133807 7.580901 0.0000 AR(2) -0.200178 0.071251 -2.809487 0.0069 R-squared 0.999914 Mean dependent var 9.893207 Adjusted R-squared 0.999906 S.D. dependent var 1.254910 S.E. of regression 0.012173 Akaike info criterion -5.883108 Sum squared resid 0.007853 Schwarz criterion -5.671833 Log likelihood 179.5517 Hannan-Quinn criter. -5.800635 F-statistic 123275.3 Durbin-Watson stat 2.266347 Prob(F-statistic) 0.000000 Inverted AR Roots .75 .27


Autocorrelation and Partial Autocorrelation Test for the Goods Model

Sample: 1954 2012

Included observations: 59 Q-statistic probabilities adjustedfor 2 ARMA term(s)

Autocorrelation Partial Correlation AC PAC Q-Stat Prob

.*| . | .*| . | 1 -0.143 -0.143 1.2681

. | . | . | . | 2 -0.015 -0.036 1.2828

. | . | . | . | 3 0.035 0.028 1.3612 0.243

. | . | . | . | 4 0.015 0.024 1.3760 0.503

Histogram of the residual of the Goods Model (Normality Statistics)

Serial Correlation Test for the Goods model

Breusch-Godfrey Serial Correlation LM Test: F-statistic 3.120295 Prob. F(4,49) 0.0230 Obs*R-squared 11.97748 Prob. Chi-Square(4) 0.0175 Test Equation: Dependent Variable: RESID Method: Least Squares Sample: 1954 2012 Included observations: 59 Presample missing value lagged residuals set to zero.


Variable Coefficient Std. Error t-Statistic Prob. C -0.368139 0.193681 -1.900749 0.0632 LOG(RTHETA) -0.055678 0.064294 -0.865983 0.3907 LOG(RGOODS(-1)) 0.086535 0.075461 1.146744 0.2571 NRL 6.63E-05 0.001202 0.055124 0.9563 AR(1) 1.143448 0.350051 3.266518 0.0020 AR(2) -0.450763 0.144036 -3.129512 0.0029 RESID(-1) -1.493218 0.424889 -3.514369 0.0010 RESID(-2) -0.885166 0.294166 -3.009064 0.0041 RESID(-3) -0.539726 0.226359 -2.384381 0.0210 RESID(-4) -0.339360 0.177546 -1.911391 0.0618 R-squared 0.203008 Mean dependent var 1.51E-12 Adjusted R-squared 0.056622 S.D. dependent var 0.011636 S.E. of regression 0.011302 Akaike info criterion -5.974426 Sum squared resid 0.006259 Schwarz criterion -5.622301 Log likelihood 186.2456 Hannan-Quinn criter. -5.836971 F-statistic 1.386798 Durbin-Watson stat 1.890445 Prob(F-statistic) 0.219932

Heteroskedasticity Test for the Goods Model

Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 4.035032 Prob. F(3,55) 0.0115 Obs*R-squared 10.64302 Prob. Chi-Square(3) 0.0138 Scaled explained SS 14.57346 Prob. Chi-Square(3) 0.0022

Test Equation: Dependent Variable: RESID^2 Method: Least Squares Date: 06/04/13 Time: 13:30 Sample: 1954 2012 Included observations: 59 Variable Coefficient Std. Error t-Statistic Prob. C 0.001533 0.000432 3.548831 0.0008 LOG(RTHETA) 0.000423 0.000189 2.237267 0.0293 LOG(RGOODS(-1)) -0.000526 0.000211 -2.496793 0.0156 NRL -3.27E-06 1.92E-05 -0.170151 0.8655 R-squared 0.180390 Mean dependent var 0.000133 Adjusted R-squared 0.135684 S.D. dependent var 0.000247 S.E. of regression 0.000230 Akaike info criterion -13.85227 Sum squared resid 2.91E-06 Schwarz criterion -13.71142 Log likelihood 412.6420 Hannan-Quinn criter. -13.79729 F-statistic 4.035032 Durbin-Watson stat 1.260730 Prob(F-statistic) 0.011522


Parameter Stability Test of the Goods Model (Chow Test at the year 1993)

Chow Breakpoint Test: 1993 Null Hypothesis: No breaks at specified breakpoints Equation Sample: 1954 2012 F-statistic 3.568746 Prob. F(6,47) 0.0054 Log likelihood ratio 22.14905 Prob. Chi-Square(6) 0.0011 Wald Statistic 35.67904 Prob. Chi-Square(6) 0.0000

Graph of the Goods Model and its Residuals

Goods Model LOG(RGOODS) = 0.415732653136 + 0.132093845226*LOG(RTHETA) + 0.846624437726*LOG(RGOODS(-1)) - 0.00150728719993*NRL + [AR(1)=1.01437993182,AR(2)=-0.200177966077]

Estimation of the Money Model using LS methodDependent Variable: LOG(RM1) Method: Least Squares Sample (adjusted): 1952 2012 Included observations: 61 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C -1.187237 0.260809 -4.552131 0.0000 LOG(RGDP_FC) 0.427156 0.097963 4.360376 0.0001 NMM_RATE1 -0.007700 0.002834 -2.716801 0.0087 LOG(RM1(-1)) 0.645233 0.086595 7.451153 0.0000 R-squared 0.995019 Mean dependent var 7.471184 Adjusted R-squared 0.994757 S.D. dependent var 0.930089 S.E. of regression 0.067344 Akaike info criterion -2.494674 Sum squared resid 0.258509 Schwarz criterion -2.356256 Log likelihood 80.08755 Hannan-Quinn criter. -2.440427 F-statistic 3795.858 Durbin-Watson stat 1.916985 Prob(F-statistic) 0.000000


Autocorrelation and Partial Autocorrelation Test for the Money Model

Sample: 1952 2012 Included observations: 61

Autocorrelation Partial Correlation AC PAC Q-Stat Prob . | . | . | . | 1 -0.017 -0.017 0.0190 0.890 . |*. | . |*. | 2 0.103 0.103 0.7144 0.700 . |*. | . |*. | 3 0.144 0.149 2.0880 0.554 . | . | . | . | 4 -0.018 -0.023 2.1105 0.715

Histogram of the residual of the Money Model (Normality Statistics)

Serial Correlation Test for the Money model

Breusch-Godfrey Serial Correlation LM Test: F-statistic 0.702069 Prob. F(4,53) 0.5940 Obs*R-squared 3.069526 Prob. Chi-Square(4) 0.5463 Test Equation: Dependent Variable: RESID Method: Least Squares Sample: 1952 2012 Included observations: 61 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic Prob. C -0.320888 0.415278 -0.772706 0.4431 LOG(RGDP_FC) 0.140200 0.169062 0.829286 0.4107 NMM_RATE1 -0.001238 0.003308 -0.374274 0.7097 LOG(RM1(-1)) -0.129281 0.152586 -0.847267 0.4007 RESID(-1) 0.093337 0.200945 0.464493 0.6442 RESID(-2) 0.190586 0.161975 1.176637 0.2446 RESID(-3) 0.227520 0.158447 1.435931 0.1569 RESID(-4) 0.028600 0.152636 0.187375 0.8521


R-squared 0.050320 Mean dependent var -7.95E-16 Adjusted R-squared -0.075109 S.D. dependent var 0.065639 S.E. of regression 0.068059 Akaike info criterion -2.415157 Sum squared resid 0.245501 Schwarz criterion -2.138321 Log likelihood 81.66228 Hannan-Quinn criter. -2.306662 F-statistic 0.401183 Durbin-Watson stat 1.915257 Prob(F-statistic) 0.897364

Heteroskedasticity Test for the Money Model


F-statistic 1.443155 Prob. F(3,57) 0.2398 Obs*R-squared 4.306208 Prob. Chi-Square(3) 0.2302 Scaled explained SS 5.887910 Prob. Chi-Square(3) 0.1172

Test Equation: Dependent Variable: RESID^2 Method: Least Squares Sample: 1952 2012 Included observations: 61

Variable Coefficient Std. Error t-Statistic Prob. C 0.057537 0.028967 1.986305 0.0518 LOG(RGDP_FC) -0.016730 0.010880 -1.537644 0.1297 NMM_RATE1 0.000411 0.000315 1.305491 0.1970 LOG(RM1(-1)) 0.013132 0.009618 1.365429 0.1775 R-squared 0.070594 Mean dependent var 0.004238 Adjusted R-squared 0.021677 S.D. dependent var 0.007562 S.E. of regression 0.007480 Akaike info criterion -6.889946 Sum squared resid 0.003189 Schwarz criterion -6.751528 Log likelihood 214.1433 Hannan-Quinn criter. -6.835698 F-statistic 1.443155 Durbin-Watson stat 1.626851 Prob(F-statistic) 0.239759

Parameter Stability Test of the Money Model (Chow Test at the year 1993) Chow Breakpoint Test: 1993 Null Hypothesis: No breaks at specified breakpoints Varying regressors: All equation variables Equation Sample: 1952 2012 F-statistic 0.573339 Prob. F(4,53) 0.6831 Log likelihood ratio 2.584013 Prob. Chi-Square(4) 0.6297 Wald Statistic 2.293357 Prob. Chi-Square(4) 0.6820


Graph of the Money Model and its Residuals

Money Model LOG(RM1) = -1.18723669959 + 0.427155545847*LOG(RGDP_FC) - 0.00769987880073*NMM_RATE1 + 0.645232731662*LOG(RM1(-1)) Estimation of the Export Model using LS method

Dependent Variable: LOG(REXPORT) Method: Least Squares Sample (adjusted): 1952 2012 Included observations: 61 after adjustments


C -0.314100 0.084043 -3.737391 0.0004 LOG(WT_2004) 0.158486 0.051214 3.094555 0.0031 LOG(REXPORT(-1)) 0.917066 0.038323 23.92974 0.0000 WNRL -0.015390 0.006924 -2.222510 0.0302

R-squared 0.995872 Mean dependent var 6.653901 Adjusted R-squared 0.995654 S.D. dependent var 1.336547 S.E. of regression 0.088107 Akaike info criterion -1.957196 Sum squared resid 0.442486 Schwarz criterion -1.818778 Log likelihood 63.69448 Hannan-Quinn criter. -1.902949 F-statistic 4583.286 Durbin-Watson stat 2.296438 Prob(F-statistic) 0.000000

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Autocorrelation and Partial Autocorrelation Test for the Export Model



.*| . | .*| . | 1 -0.165 -0.165 1.7469 0.186

.*| . | **| . | 2 -0.187 -0.220 4.0194 0.134

.*| . | .*| . | 3 -0.068 -0.154 4.3289 0.228

. |*. | . | . | 4 0.135 0.052 5.5618 0.234

Histogram of the residual of the Export Model (Normality Statistics)

Serial Correlation Test for the Export model Breusch-Godfrey Serial Correlation LM Test:

F-statistic 1.660060 Prob. F(4,53) 0.1730 Obs*R-squared 6.791635 Prob. Chi-Square(4) 0.1473

Test Equation: Dependent Variable: RESID Method: Least Squares Sample: 1952 2012 Included observations: 61 Presample missing value lagged residuals set to zero.


C 0.028015 0.083014 0.337473 0.7371 LOG(WT_2004) -0.038635 0.052447 -0.736650 0.4646 LOG(REXPORT(-1)) 0.030661 0.039440 0.777423 0.4404 WNRL 0.003219 0.007029 0.457982 0.6488 RESID(-1) -0.266710 0.144284 -1.848500 0.0701 RESID(-2) -0.266520 0.144011 -1.850691 0.0698 RESID(-3) -0.174580 0.150473 -1.160207 0.2512 RESID(-4) 0.041241 0.149530 0.275805 0.7838



Heteroskedasticity Test for the Export Model

Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 1.709763 Prob. F(3,57) 0.1752 Obs*R-squared 5.036057 Prob. Chi-Square(3) 0.1692 Scaled explained SS 5.105833 Prob. Chi-Square(3) 0.1642



C 0.024976 0.010448 2.390516 0.0202 LOG(WT_2004) -0.001708 0.006367 -0.268329 0.7894 LOG(REXPORT(-1)) -0.000402 0.004764 -0.084369 0.9331 WNRL -0.000735 0.000861 -0.853530 0.3969 R-squared 0.082558 Mean dependent var 0.007254 Adjusted R-squared 0.034272 S.D. dependent var 0.011146 S.E. of regression 0.010953 Akaike info criterion -6.127030 Sum squared resid 0.006839 Schwarz criterion -5.988612 Log likelihood 190.8744 Hannan-Quinn criter. -6.072783 F-statistic 1.709763 Durbin-Watson stat 2.012531 Prob(F-statistic) 0.175201

Parameter Stability Test of the Export Model (Chow Test at the year 1993)Chow Breakpoint Test: 1993 Null Hypothesis: No breaks at specified breakpoints Varying regressors: All equation variables Equation Sample: 1952 2012

F-statistic 0.820397 Prob. F(4,53) 0.5180 Log likelihood ratio 3.664608 Prob. Chi-Square(4) 0.4533 Wald Statistic 3.281588 Prob. Chi-Square(4) 0.5119


Graph of the Export Model and its Residuals

Export Model LOG(REXPORT) = -0.314099953745 + 0.158485572564*LOG(WT_2004) + 0.917065669929*LOG(REXPORT(-1)) - 0.0153895005844*WNRL Estimation of the Import Model using LS method

Dependent Variable: LOG(RIMPORT) Method: Least Squares Sample (adjusted): 1952 2012 Included observations: 61 after adjustments


C -0.404526 0.756921 -0.534436 0.5952 LOG(RGDP_FC) 0.324695 0.111290 2.917543 0.0051 LOG(REER) -0.198863 0.103058 -1.929616 0.0587 WNRL -0.009561 0.007365 -1.298188 0.1995 LOG(RIMPORT(-1)) 0.783656 0.077685 10.08755 0.0000



Autocorrelation and Partial Autocorrelation Test for the Import Model



. | . | . | . | 1 -0.040 -0.040 0.1044 0.747 .*| . | .*| . | 2 -0.110 -0.112 0.8927 0.640 . | . | . | . | 3 0.022 0.012 0.9238 0.820 .*| . | .*| . | 4 -0.078 -0.090 1.3377 0.855

Histogram of the residual of the Import Model (Normality Statistics) �

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Serial Correlation Test for the Import model

Breusch-Godfrey Serial Correlation LM Test: F-statistic 0.951045 Prob. F(4,52) 0.4422 Obs*R-squared 4.158381 Prob. Chi-Square(4) 0.3850

Test Equation: Dependent Variable: RESID Method: Least Squares Sample: 1952 2012 Included observations: 61 Presample missing value lagged residuals set to zero.

Variable Coefficient Std. Error t-Statistic Prob. C 0.869854 0.904279 0.961931 0.3405 LOG(RGDP_FC) -0.377478 0.239958 -1.573097 0.1218 LOG(REER) 0.125532 0.125611 0.999373 0.3222 WNRL 0.016020 0.011646 1.375594 0.1748 LOG(RIMPORT(-1)) 0.276753 0.174271 1.588061 0.1183 RESID(-1) -0.368570 0.244919 -1.504862 0.1384 RESID(-2) -0.391758 0.216888 -1.806265 0.0767 RESID(-3) -0.162721 0.174664 -0.931621 0.3558 RESID(-4) -0.175038 0.146730 -1.192927 0.2383



Heteroskedasticity Test for the Import Model


F-statistic 5.089581 Prob. F(4,56) 0.0014 Obs*R-squared 16.26356 Prob. Chi-Square(4) 0.0027 Scaled explained SS 13.55377 Prob. Chi-Square(4) 0.0089


Variable Coefficient Std. Error t-Statistic Prob. C 0.266583 0.107911 2.470407 0.0166 LOG(RGDP_FC) -0.048087 0.015866 -3.030800 0.0037 LOG(REER) 0.001156 0.014693 0.078693 0.9376 WNRL 0.000307 0.001050 0.292409 0.7711 LOG(RIMPORT(-1)) 0.026590 0.011075 2.400827 0.0197 R-squared 0.266616 Mean dependent var 0.014013 Adjusted R-squared 0.214231 S.D. dependent var 0.019870 S.E. of regression 0.017614 Akaike info criterion -5.161856 Sum squared resid 0.017374 Schwarz criterion -4.988833 Log likelihood 162.4366 Hannan-Quinn criter. -5.094047 F-statistic 5.089581 Durbin-Watson stat 1.072189 Prob(F-statistic) 0.001435

Parameter Stability Test of the Import Model (Chow Test at the year 1993)

Chow Breakpoint Test: 1993 Null Hypothesis: No breaks at specified breakpoints Varying regressors: All equation variables Equation Sample: 1952 2012

F-statistic 2.548978 Prob. F(5,51) 0.0391 Log likelihood ratio 13.60687 Prob. Chi-Square(5) 0.0183 Wald Statistic 12.74489 Prob. Chi-Square(5) 0.0259


Graph of the Import Model and its Residuals

Import Model LOG(RIMPORT) = -0.404525837355 + 0.324694556351*LOG(RGDP_FC) - 0.198862895413*LOG(REER) - 0.00956094050808*WNRL + 0.783655795559*LOG(RIMPORT(-1)) Estimation of the Real Exchange Rate Model using LS method

Dependent Variable: LOG(REER) Method: Least Squares Sample (adjusted): 1952 2012 Included observations: 61 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C 0.419430 0.240959 1.740669 0.0871 LOG(RW0405) -0.059403 0.028509 -2.083660 0.0417 LOG(RCORP_TAX) 0.063679 0.035333 1.802273 0.0768 LOG(REER(-1)) 0.877817 0.054407 16.13426 0.0000



Autocorrelation and Partial Autocorrelation Test for the Real Exchange Rate Model


Autocorrelation Partial Correlation AC PAC Q-Stat Prob . |*. | . |*. | 1 0.135 0.135 1.1665 0.280 . | . | . | . | 2 0.045 0.027 1.2956 0.523 . |*. | . |*. | 3 0.129 0.122 2.3953 0.495 .*| . | .*| . | 4 -0.095 -0.133 3.0080 0.556

Histogram of the residual of the Real Exchange Rate Model (Normality Statistics)

Serial Correlation Test for the Real Exchange Rate model

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.845478 Prob. F(4,53) 0.5027 Obs*R-squared 3.658917 Prob. Chi-Square(4) 0.4541

Test Equation: Dependent Variable: RESID Method: Least Squares Date: 06/04/13 Time: 14:15 Sample: 1952 2012 Included observations: 61 Presample missing value lagged residuals set to zero.

Variable Coefficient Std. Error t-Statistic Prob. C 0.169996 0.305091 0.557197 0.5797LOG(RW0405) -0.014665 0.030384 -0.482661 0.6313LOG(RCORP_TAX) 0.014853 0.036884 0.402700 0.6888LOG(REER(-1)) -0.042470 0.067690 -0.627415 0.5331RESID(-1) 0.180889 0.147149 1.229293 0.2244RESID(-2) 0.038476 0.145208 0.264970 0.7921RESID(-3) 0.168419 0.144661 1.164233 0.2495RESID(-4) -0.127564 0.149582 -0.852800 0.3976



Heteroskedasticity Test for the Real Exchange Rate Model

Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 1.099716 Prob. F(3,57) 0.3568 Obs*R-squared 3.337493 Prob. Chi-Square(3) 0.3425 Scaled explained SS 2.943225 Prob. Chi-Square(3) 0.4005 Test Equation: Dependent Variable: RESID^2 Method: Least Squares Sample: 1952 2012 Included observations: 61

Variable Coefficient Std. Error t-Statistic Prob. C -0.018620 0.019674 -0.946444 0.3479 LOG(RW0405) 0.001195 0.002328 0.513213 0.6098 LOG(RCORP_TAX) -0.001558 0.002885 -0.539899 0.5914 LOG(REER(-1)) 0.005478 0.004442 1.233094 0.2226 R-squared 0.054713 Mean dependent var 0.003491 Adjusted R-squared 0.004961 S.D. dependent var 0.005003 S.E. of regression 0.004991 Akaike info criterion -7.699167 Sum squared resid 0.001420 Schwarz criterion -7.560749 Log likelihood 238.8246 Hannan-Quinn criter. -7.644920 F-statistic 1.099716 Durbin-Watson stat 1.991662 Prob(F-statistic) 0.356793

Parameter Stability Test of the Real Exchange Rate Model (Chow Test at the year 1993)

Chow Breakpoint Test: 1993 Null Hypothesis: No breaks at specified breakpoints Varying regressors: All equation variables Equation Sample: 1952 2012 F-statistic 1.876888 Prob. F(4,53) 0.1281 Log likelihood ratio 8.081052 Prob. Chi-Square(4) 0.0887 Wald Statistic 7.507552 Prob. Chi-Square(4) 0.1114


Graph of the Real Exchange Rate Model and its Residuals

Real Exchange Rate Model LOG(REER) = 0.419429927406 - 0.0594029631419*LOG(RW0405) + 0.0636788780546*LOG(RCORP_TAX) + 0.877817487337*LOG(REER(-1)) Estimation of the Employment Model using LS method

Dependent Variable: LOG(EMPLOYMENT) Method: Least Squares Sample (adjusted): 1952 2012 Included observations: 61 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. C -2.549946 0.634954 -4.015951 0.0002 LOG(TU) -0.095371 0.043886 -2.173141 0.0340 LOG(RW0405) -0.139162 0.034003 -4.092698 0.0001 LOG(WPOP) 0.768298 0.197635 3.887455 0.0003 LOG(EMPLOYMENT(-1)) 0.791510 0.058981 13.41978 0.0000 R-squared 0.995343 Mean dependent var 3.003635 Adjusted R-squared 0.995010 S.D. dependent var 0.348023 S.E. of regression 0.024583 Akaike info criterion -4.495082 Sum squared resid 0.033843 Schwarz criterion -4.322060 Log likelihood 142.1000 Hannan-Quinn criter. -4.427273 F-statistic 2992.240 Durbin-Watson stat 2.555134 Prob(F-statistic) 0.000000


Autocorrelation and Partial Autocorrelation Test for the Employment Model



. | . | . | . | 1 0.065 0.065 0.2748 0.600 . |** | . |** | 2 0.278 0.275 5.3167 0.070 . | . | . | . | 3 0.060 0.031 5.5562 0.135 . | . | .*| . | 4 -0.016 -0.105 5.5730 0.233

Histogram of the residual of the Employment Model (Normality Statistics)

Serial Correlation Test for the Employment model

Breusch-Godfrey Serial Correlation LM Test: F-statistic 18.27547 Prob. F(4,52) 0.0000 Obs*R-squared 9.066212 Prob. Chi-Square(4) 0.0595 Test Equation: Dependent Variable: RESID Method: Least Squares Sample: 1952 2012 Included observations: 61 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic Prob. C 0.876956 0.527755 1.661672 0.1026 LOG(TU) 0.003167 0.033405 0.094796 0.9248 LOG(RW0405) 0.041342 0.026881 1.537931 0.1301 LOG(WPOP) -0.231326 0.154568 -1.496595 0.1405 LOG(EMPLOYMENT(-1)) 0.117077 0.066247 1.767296 0.0830 RESID(-1) -0.078675 0.169574 -0.463955 0.6446 RESID(-2) 0.130344 0.165248 0.788778 0.4338 RESID(-3) -0.080017 0.151806 -0.527097 0.6004 RESID(-4) -0.205936 0.149046 -1.381695 0.1730



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Heteroskedasticity Test for the Employment Model

Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 0.575160 Prob. F(4,56) 0.6818 Obs*R-squared 2.407163 Prob. Chi-Square(4) 0.6613 Scaled explained SS 0.762811 Prob. Chi-Square(4) 0.9434 Test Equation: Dependent Variable: RESID^2 Method: Least Squares Sample: 1952 2012 Included observations: 61 Variable Coefficient Std. Error t-Statistic Prob. C -0.008474 0.012700 -0.667207 0.5074 LOG(TU) -0.000698 0.000878 -0.794532 0.4302 LOG(RW0405) -0.000469 0.000680 -0.689233 0.4935 LOG(WPOP) 0.002815 0.003956 0.711508 0.4797 LOG(EMPLOYMENT(-1)) 0.000192 0.001171 0.164370 0.8700 R-squared 0.039462 Mean dependent var 0.000271 Adjusted R-squared -0.029148 S.D. dependent var 0.000485 S.E. of regression 0.000492 Akaike info criterion -12.31724 Sum squared resid 1.36E-05 Schwarz criterion -12.14422 Log likelihood 380.6758 Hannan-Quinn criter. -12.24943 F-statistic 0.575160 Durbin-Watson stat 1.536203 Prob(F-statistic) 0.681787

Parameter Stability Test of the Employment Model (Chow Test at the year 1993)



Graph of the Employment and its Residuals

Employment Model ========================= LOG(EMPLOYMENT) = -2.54994565336 - 0.0953710881258*LOG(TU) - 0.139161993159*LOG(RW0405) + 0.768297775237*LOG(WPOP) + 0.791509916343*LOG(EMPLOYMENT(-1)) Estimation of the Real Wages Model using LS method

Dependent Variable: LOG(RW0405) Method: Least Squares Sample (adjusted): 1952 2012 Included observations: 61 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C -4.105414 1.489493 -2.756249 0.0078 LOG(EMPLOYMENT) -0.369503 0.201966 -1.829535 0.0726 LOG(WPOP) 0.977778 0.380138 2.572168 0.0127 LOG(RW0405(-1)) 0.810088 0.073289 11.05341 0.0000 R-squared 0.998379 Mean dependent var 2.092731 Adjusted R-squared 0.998294 S.D. dependent var 1.813811 S.E. of regression 0.074923 Akaike info criterion -2.281386 Sum squared resid 0.319967 Schwarz criterion -2.142968 Log likelihood 73.58227 Hannan-Quinn criter. -2.227139 F-statistic 11702.51 Durbin-Watson stat 1.973650 Prob(F-statistic) 0.000000


Autocorrelation and Partial Autocorrelation Test for the Real Wages Model

Sample: 1952 2012 Included observations: 61 Autocorrelation Partial Correlation AC PAC Q-Stat Prob . | . | . | . | 1 -0.011 -0.011 0.0073 0.932 . | . | . | . | 2 -0.042 -0.042 0.1198 0.942 .*| . | .*| . | 3 -0.080 -0.081 0.5438 0.909 . | . | . | . | 4 0.012 0.008 0.5528 0.968

Histogram of the residual of the Real Wages Model (Normality Statistics)

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Serial Correlation Test for the Real Wages model

Breusch-Godfrey Serial Correlation LM Test: F-statistic 0.342736 Prob. F(4,53) 0.8479 Obs*R-squared 3.482545 Prob. Chi-Square(4) 0.4805 Test Equation: Dependent Variable: RESID Method: Least Squares Sample: 1952 2012 Included observations: 61 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic Prob. C 4.782372 2.921250 1.637098 0.1075 LOG(EMPLOYMENT) 0.606662 0.370438 1.637687 0.1074 LOG(WPOP) -1.216524 0.740067 -1.643803 0.1061 LOG(RW0405(-1)) 0.236534 0.144428 1.637722 0.1074 RESID(-1) -0.286459 0.213948 -1.338921 0.1863 RESID(-2) -0.243663 0.182367 -1.336113 0.1872 RESID(-3) -0.221085 0.159494 -1.386163 0.1715 RESID(-4) -0.060214 0.145152 -0.414833 0.6799


R-squared 0.057091 Mean dependent var 0.000669 Adjusted R-squared -0.067444 S.D. dependent var 0.074250 S.E. of regression 0.076713 Akaike info criterion -2.175776 Sum squared resid 0.311899 Schwarz criterion -1.898940 Log likelihood 74.36118 Hannan-Quinn criter. -2.067282 F-statistic 0.458432 Durbin-Watson stat 2.065983 Prob(F-statistic) 0.859966

Heteroskedasticity Test for the Real Wages Model

Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 0.856049 Prob. F(3,57) 0.4692 Obs*R-squared 2.629878 Prob. Chi-Square(3) 0.4523 Scaled explained SS 3.102435 Prob. Chi-Square(3) 0.3761 Test Equation: Dependent Variable: RESID^2 Method: Least Squares Sample: 1952 2012 Included observations: 61 Variable Coefficient Std. Error t-Statistic Prob. C 0.120978 0.174267 0.694210 0.4904 LOG(EMPLOYMENT) 0.008100 0.023559 0.343813 0.7323 LOG(WPOP) -0.026353 0.044448 -0.592893 0.5556 LOG(RW0405(-1)) 0.006702 0.008559 0.783081 0.4368 R-squared 0.043113 Mean dependent var 0.005423 Adjusted R-squared -0.007250 S.D. dependent var 0.008694 S.E. of regression 0.008725 Akaike info criterion -6.581824 Sum squared resid 0.004340 Schwarz criterion -6.443406 Log likelihood 204.7456 Hannan-Quinn criter. -6.527577 F-statistic 0.856049 Durbin-Watson stat 1.525294 Prob(F-statistic) 0.469219

Parameter Stability Test of the Real Wages Model (Chow Test at the year 1993)



Graph of the Real Wages and its Residuals

Real Wages Model LOG(RW0405) = -4.10541428621 - 0.36950323803*LOG(EMPLOYMENT) + 0.977778469515*LOG(WPOP) + 0.810088491435*LOG(RW0405(-1)) Estimation of the Bank Credit Model using LS method

Dependent Variable: LOG(RBANK_CRED) Method: Least Squares Sample (adjusted): 1952 2012 Included observations: 61 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C -1.484083 0.964960 -1.537974 0.1297 LOG(RBANK_DEPO) 0.148856 0.071665 2.077122 0.0424 LOG(RGDP_FC) 0.195401 0.126289 1.547248 0.1274 LOG(RBANK_CRED(-1)) 0.753563 0.077107 9.772989 0.0000 NRS -0.007850 0.005340 -1.469867 0.1472 R-squared 0.998154 Mean dependent var 2.743651 Adjusted R-squared 0.998022 S.D. dependent var 1.481713 S.E. of regression 0.065897 Akaike info criterion -2.523043 Sum squared resid 0.243173 Schwarz criterion -2.350021 Log likelihood 81.95282 Hannan-Quinn criter. -2.455234 F-statistic 7569.887 Durbin-Watson stat 1.551279 Prob(F-statistic) 0.000000


Autocorrelation and Partial Autocorrelation Test for the Bank Credit Model


Autocorrelation Partial Correlation AC PAC Q-Stat Prob . |*. | . |*. | 1 0.145 0.145 1.3453 0.246 . |*. | . | . | 2 0.089 0.070 1.8624 0.394 . | . | . | . | 3 0.003 -0.020 1.8630 0.601 **| . | **| . | 4 -0.251 -0.261 6.0999 0.192

Histogram of the residual of the Bank Credit Model (Normality Statistics)

Serial Correlation Test for the Bank Credit model

Breusch-Godfrey Serial Correlation LM Test: F-statistic 1.347657 Prob. F(4,52) 0.2649 Obs*R-squared 5.729653 Prob. Chi-Square(4) 0.2203 Test Equation: Dependent Variable: RESID Method: Least Squares Sample: 1952 2012 Included observations: 61 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic Prob. C -0.265620 1.518879 -0.174879 0.8619 LOG(RBANK_DEPO) 0.006168 0.086832 0.071029 0.9436 LOG(RGDP_FC) 0.033705 0.195633 0.172286 0.8639 LOG(RBANK_CRED(-1)) -0.026398 0.157167 -0.167962 0.8673 NRS 0.001368 0.005703 0.239944 0.8113 RESID(-1) 0.154422 0.190335 0.811317 0.4209 RESID(-2) 0.107829 0.163426 0.659801 0.5123 RESID(-3) 0.034561 0.167143 0.206773 0.8370 RESID(-4) -0.246690 0.167361 -1.473999 0.1465



Heteroskedasticity Test for the Bank Credit Model

Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 2.056072 Prob. F(4,56) 0.0989 Obs*R-squared 7.811399 Prob. Chi-Square(4) 0.0987 Scaled explained SS 8.984747 Prob. Chi-Square(4) 0.0615 Test Equation: Dependent Variable: RESID^2 Method: Least Squares Sample: 1952 2012 Included observations: 61 Variable Coefficient Std. Error t-Statistic Prob. C -0.091181 0.093992 -0.970092 0.3362 LOG(RBANK_DEPO) 0.005529 0.006981 0.791990 0.4317 LOG(RGDP_FC) 0.012688 0.012301 1.031439 0.3068 LOG(RBANK_CRED(-1)) -0.013405 0.007511 -1.784843 0.0797 NRS -0.000647 0.000520 -1.244658 0.2184 R-squared 0.128056 Mean dependent var 0.003986 Adjusted R-squared 0.065774 S.D. dependent var 0.006641 S.E. of regression 0.006419 Akaike info criterion -7.180792 Sum squared resid 0.002307 Schwarz criterion -7.007770 Log likelihood 224.0142 Hannan-Quinn criter. -7.112983 F-statistic 2.056072 Durbin-Watson stat 1.675697 Prob(F-statistic) 0.098873

Parameter Stability Test of the Bank Credit Model (Chow Test at the year 1993)



Graph of the Bank Credit and its Residuals

Bank Credit Model LOG(RBANK_CRED) = -1.48408306259 + 0.148856220863*LOG(RBANK_DEPO) + 0.195401089105*LOG(RGDP_FC) + 0.753563036736*LOG(RBANK_CRED(-1)) - 0.0078495680684*NRS Estimation of the Bank Deposit Model using LS method

Dependent Variable: LOG(RBANK_DEPO) Method: Least Squares Sample (adjusted): 1952 2012 Included observations: 61 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C -2.333829 0.941043 -2.480045 0.0161 LOG(RGDP_FC) 0.280746 0.116231 2.415407 0.0189 LOG(EMPLOYMENT) 0.115727 0.062120 1.862972 0.0676 LOG(RBANK_DEPO(-1)) 0.833654 0.069382 12.01548 0.0000 R-squared 0.998192 Mean dependent var 3.150302 Adjusted R-squared 0.998097 S.D. dependent var 1.526943 S.E. of regression 0.066619 Akaike info criterion -2.516331 Sum squared resid 0.252971 Schwarz criterion -2.377913 Log likelihood 80.74811 Hannan-Quinn criter. -2.462084 F-statistic 10488.04 Durbin-Watson stat 1.721731 Prob(F-statistic) 0.000000


Autocorrelation and Partial Autocorrelation Test for the Bank Deposit Model


Autocorrelation Partial Correlation AC PAC Q-Stat Prob . |*. | . |*. | 1 0.097 0.097 0.5968 0.440 . |*. | . |*. | 2 0.094 0.086 1.1777 0.555 .*| . | .*| . | 3 -0.112 -0.130 2.0023 0.572 .*| . | . | . | 4 -0.069 -0.056 2.3207 0.677

Histogram of the residual of the Bank Deposit Model (Normality Statistics)

Serial Correlation Test for the Bank Deposit model

Breusch-Godfrey Serial Correlation LM Test: F-statistic 0.454061 Prob. F(4,53) 0.7690 Obs*R-squared 2.350303 Prob. Chi-Square(4) 0.6716 Test Equation: Dependent Variable: RESID Method: Least Squares Sample: 1952 2012 Included observations: 61 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic Prob. C -0.185146 1.686365 -0.109790 0.9130 LOG(RGDP_FC) 0.023720 0.203343 0.116649 0.9076 LOG(EMPLOYMENT) 0.003098 0.089281 0.034700 0.9724 LOG(RBANK_DEPO(-1)) -0.013795 0.126469 -0.109079 0.9136 RESID(-1) 0.108693 0.180543 0.602035 0.5497 RESID(-2) 0.116631 0.166350 0.701117 0.4863 RESID(-3) -0.120942 0.166841 -0.724892 0.4717 RESID(-4) -0.052066 0.164654 -0.316214 0.7531 R-squared 0.038530 Mean dependent var -0.000209 Adjusted R-squared -0.088457 S.D. dependent var 0.065114 S.E. of regression 0.067933 Akaike info criterion -2.418878 Sum squared resid 0.244589 Schwarz criterion -2.142043 Log likelihood 81.77579 Hannan-Quinn criter. -2.310384 F-statistic 0.303414 Durbin-Watson stat 1.923293 Prob(F-statistic) 0.949271


Heteroskedasticity Test for the Bank Deposit Model Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 5.673932 Prob. F(3,57) 0.0018 Obs*R-squared 14.02735 Prob. Chi-Square(3) 0.0029 Scaled explained SS 15.13449 Prob. Chi-Square(3) 0.0017 Test Equation: Dependent Variable: RESID^2 Method: Least Squares Sample: 1952 2012 Included observations: 61 Variable Coefficient Std. Error t-Statistic Prob. C 0.099628 0.083658 1.190893 0.2386 LOG(RGDP_FC) -0.008227 0.010334 -0.796124 0.4293 LOG(EMPLOYMENT) -0.011894 0.005504 -2.161004 0.0349 LOG(RBANK_DEPO(-1)) 0.005173 0.006171 0.838326 0.4053 R-squared 0.229957 Mean dependent var 0.004170 Adjusted R-squared 0.189428 S.D. dependent var 0.006573 S.E. of regression 0.005918 Akaike info criterion -7.358236 Sum squared resid 0.001996 Schwarz criterion -7.219818 Log likelihood 228.4262 Hannan-Quinn criter. -7.303989 F-statistic 5.673932 Durbin-Watson stat 1.958866 Prob(F-statistic) 0.001798

Parameter Stability Test of the Bank Deposit Model (Chow Test at the year 1993)


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Graph of the Bank Deposit and its Residuals

Bank Deposit Model LOG(RBANK_DEPO) = -2.33382871625 + 0.280746032388*LOG(RGDP_FC) + 0.115726933624*LOG(EMPLOYMENT) + 0.833654453902*LOG(RBANK_DEPO(-1)) Overall, the parameter estimates for the behavioural equations look satisfactory. These coefficients conform well to theory and all variables have the expected a priori sign. Having got OLS estimates of the behavioral equation, we put these equations as a system and estimate parameters simultaneously. The system of 10 behavioral equations was estimated using the Three-Stage Least Squares method which takes the estimates of the least square equations as the starting point. The Three-Stage Least Squares estimated coefficient reported along with statistical properties of each of the equation. The residuals of each of the equations were put in a graph for a visual test.


Equation: LOG(RCONSU) = K(1) + K(2)*LOG(RGDP_FC)+ [AR(1)=K(3), AR(2)=K(4)] Instruments: LOG(RAIN_MM) NMM_RATE1 WNRL LOG(WT_2004) LOG(RCORP_TAX) LOG(WPOP) LOG(RCONSU(-1)) LOG(RGOODS( -1)) LOG(RM1(-1)) LOG(RIMPORT(-1)) LOG(REXPORT(-1)) LOG(REER(-1)) LOG(RW0405(-1)) LOG(EMPLOYMENT(-1)) C LOG(RGDP_FC(-1)) LOG(RCONSU(-2)) LOG(RGDP_FC(-2)) Observations: 60 R-squared 0.999765 Mean dependent var 8.945388 Adjusted R-squared 0.999752 S.D. dependent var 0.701494 S.E. of regression 0.011039 Sum squared resid 0.006824 Durbin-Watson stat 2.128826 Equation: LOG(RGOODS) = C(1) + C(2)*LOG(RTHETA) + C(3) *LOG(RGOODS(-1)) + C(4)*NRL + [AR(1)=C(5),AR(2)=C(6)] Instruments: LOG(RAIN_MM) NMM_RATE1 WNRL LOG(WT_2004) LOG(RCORP_TAX) LOG(WPOP) LOG(RCONSU(-1)) LOG(RGOODS( -1)) LOG(RM1(-1)) LOG(RIMPORT(-1)) LOG(REXPORT(-1)) LOG(REER(-1)) LOG(RW0405(-1)) LOG(EMPLOYMENT(-1)) C LOG(RTHETA(-1)) LOG(RGOODS(-2)) NRL(-1) LOG(RTHETA(-2)) LOG(RGOODS(-3)) NRL(-2) Observations: 59 R-squared 0.999907 Mean dependent var 9.893207 Adjusted R-squared 0.999898 S.D. dependent var 1.254910 S.E. of regression 0.012642 Sum squared resid 0.008471 Durbin-Watson stat 2.166970 Equation: LOG(RM1) = M(1) + M(2)*LOG(RGDP_FC) + M(3)*NMM_RATE1 + M(4)*LOG(RM1(-1)) Instruments: LOG(RAIN_MM) NMM_RATE1 WNRL LOG(WT_2004) LOG(RCORP_TAX) LOG(WPOP) LOG(RCONSU(-1)) LOG(RGOODS( -1)) LOG(RM1(-1)) LOG(RIMPORT(-1)) LOG(REXPORT(-1)) LOG(REER(-1)) LOG(RW0405(-1)) LOG(EMPLOYMENT(-1)) C Observations: 61 R-squared 0.994816 Mean dependent var 7.471184 Adjusted R-squared 0.994543 S.D. dependent var 0.930089 S.E. of regression 0.068708 Sum squared resid 0.269085 Durbin-Watson stat 2.117765 Equation: LOG(RIMPORT) = I(1) + I(2)*LOG(RGDP_FC) + I(3)*LOG(REER) + I(4)*WNRL + I(5)*LOG(RIMPORT(-1)) Instruments: LOG(RAIN_MM) NMM_RATE1 WNRL LOG(WT_2004) LOG(RCORP_TAX) LOG(WPOP) LOG(RCONSU(-1)) LOG(RGOODS( -1)) LOG(RM1(-1)) LOG(RIMPORT(-1)) LOG(REXPORT(-1)) LOG(REER(-1)) LOG(RW0405(-1)) LOG(EMPLOYMENT(-1)) C Observations: 61 R-squared 0.991284 Mean dependent var 6.909606 Adjusted R-squared 0.990661 S.D. dependent var 1.288205 S.E. of regression 0.124488 Sum squared resid 0.867851 Durbin-Watson stat 1.842556


Equation: LOG(REXPORT) = T(1) + T(2)*LOG(WT_2004) + T(3) *LOG(REXPORT(-1)) + T(4)*WNRL Instruments: LOG(RAIN_MM) NMM_RATE1 WNRL LOG(WT_2004) LOG(RCORP_TAX) LOG(WPOP) LOG(RCONSU(-1)) LOG(RGOODS( -1)) LOG(RM1(-1)) LOG(RIMPORT(-1)) LOG(REXPORT(-1)) LOG(REER(-1)) LOG(RW0405(-1)) LOG(EMPLOYMENT(-1)) C Observations: 61 R-squared 0.995855 Mean dependent var 6.653901 Adjusted R-squared 0.995637 S.D. dependent var 1.336547 S.E. of regression 0.088283 Sum squared resid 0.444248 Durbin-Watson stat 2.254275 Equation: LOG(REER) = X(1) + X(2)*LOG(RW0405) + X(3) *LOG(RCORP_TAX) + X(4)*LOG(REER(-1)) Instruments: LOG(RAIN_MM) NMM_RATE1 WNRL LOG(WT_2004) LOG(RCORP_TAX) LOG(WPOP) LOG(RCONSU(-1)) LOG(RGOODS( -1)) LOG(RM1(-1)) LOG(RIMPORT(-1)) LOG(REXPORT(-1)) LOG(REER(-1)) LOG(RW0405(-1)) LOG(EMPLOYMENT(-1)) C Observations: 61 R-squared 0.945946 Mean dependent var 4.896706 Adjusted R-squared 0.943101 S.D. dependent var 0.256589 S.E. of regression 0.061205 Sum squared resid 0.213528 Durbin-Watson stat 1.700623 Equation: LOG(RW0405) = W(1) + W(2)*LOG(EMPLOYMENT) + W(3) *LOG(WPOP) + W(4)*LOG(RW0405(-1)) Instruments: LOG(RAIN_MM) NMM_RATE1 WNRL LOG(WT_2004) LOG(RCORP_TAX) LOG(WPOP) LOG(RCONSU(-1)) LOG(RGOODS( -1)) LOG(RM1(-1)) LOG(RIMPORT(-1)) LOG(REXPORT(-1)) LOG(REER(-1)) LOG(RW0405(-1)) LOG(EMPLOYMENT(-1)) C Observations: 61 R-squared 0.998323 Mean dependent var 2.092731 Adjusted R-squared 0.998234 S.D. dependent var 1.813811 S.E. of regression 0.076217 Sum squared resid 0.331119 Durbin-Watson stat 2.145771 Equation: LOG(EMPLOYMENT) = P(1) + P(2)*LOG(TU) + P(3) *LOG(RW0405) + P(4)*LOG(WPOP) + P(5)*LOG(EMPLOYMENT(-1)) Instruments: LOG(RAIN_MM) NMM_RATE1 WNRL LOG(WT_2004) LOG(RCORP_TAX) LOG(WPOP) LOG(RCONSU(-1)) LOG(RGOODS( -1)) LOG(RM1(-1)) LOG(RIMPORT(-1)) LOG(REXPORT(-1)) LOG(REER(-1)) LOG(RW0405(-1)) LOG(EMPLOYMENT(-1)) C Observations: 61 R-squared 0.997891 Mean dependent var 3.005445 Adjusted R-squared 0.997740 S.D. dependent var 0.349449 S.E. of regression 0.016612 Sum squared resid 0.015454 Durbin-Watson stat 2.230883 Equation: LOG(RBANK_CRED) = C(7) + C(8)*LOG(RBANK_DEPO) + C(9) *LOG(RGDP_FC) + C(10)*LOG(RBANK_CRED(-1)) + C(11)*NRS


Instruments: LOG(RAIN_MM) NMM_RATE1 WNRL LOG(WT_2004) LOG(RCORP_TAX) LOG(WPOP) LOG(RCONSU(-1)) LOG(RGOODS( -1)) LOG(RM1(-1)) LOG(RIMPORT(-1)) LOG(REXPORT(-1)) LOG(REER(-1)) LOG(RW0405(-1)) LOG(EMPLOYMENT(-1)) C Observations: 61 R-squared 0.997896 Mean dependent var 2.743651 Adjusted R-squared 0.997746 S.D. dependent var 1.481714 S.E. of regression 0.070347 Sum squared resid 0.277130 Durbin-Watson stat 1.846337 Equation: LOG(RBANK_DEPO) = C(12) + C(13)*LOG(RGDP_FC) + C(14) *LOG(EMPLOYMENT) + C(15)*LOG(RBANK_DEPO(-1)) Instruments: LOG(RAIN_MM) NMM_RATE1 WNRL LOG(WT_2004) LOG(RCORP_TAX) LOG(WPOP) LOG(RCONSU(-1)) LOG(RGOODS( -1)) LOG(RM1(-1)) LOG(RIMPORT(-1)) LOG(REXPORT(-1)) LOG(REER(-1)) LOG(RW0405(-1)) LOG(EMPLOYMENT(-1)) C Observations: 61 R-squared 0.998162 Mean dependent var 3.150302 Adjusted R-squared 0.998066 S.D. dependent var 1.526943 S.E. of regression 0.067157 Sum squared resid 0.257075 Durbin-Watson stat 1.800264

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APPENDIX 4 The Model Data Description

CRR [Description] Source: T46 and CRR-SLR Calc EMPLOYMENT [Description] Source:Indiastat.com and Ess EMPLOYMENT_0 [Display Name] EMPLOYMENT (Baseline) [Description] Source:Indias GDP_DEF04_05 [Display Name] GDP-Def04-05 [History] calc using GDP at 2004-5 p INFL [History] Modified: 1951 2012 // infl=(d(wpi04_05))*100 INFL_0 [Display Name] INFL (Baseline) [History] Modified: 2013 2018 // N_CG_INTEREST_PAYMENT [Display Name] N-CG-interest payment [History] Source: T103 N_GOVT_CAPEX [Display Name] N-Govt-Capex [History] Nominal Central Govt Capi NAFC [Description] T1 [History] Nominal AFC in Rs Billion NBANK_CREDIT [Display Name] Nbank Credit [History] Nominal Bank Credit Rs Bil NBANK_DEPOSIT [Display Name] Nbank Deposit [History] Nominal Bank Aggr Deposit NCORP_TAX [Display Name] Ncorp Tax [History] Nominal Corporation tax in R NEXCISE_TAX [Display Name] Nexcise Tax [History] Nominal Excise duties (net) NEXPORTS [Description] T128 and Indiastat.com and ES NFISCAL_DEFICT [Display Name] Nfiscal Defict [History] Nominal Central govt Gro NFOR_ASSETS [Display Name] Nfor-Assets [History] Nominal Forex Assets in Rs NFOREX_RES [Display Name] NForex Res [History] Foreign Exch Reserves (Rupee NGDP_FC [Display Name] NGDP-FC [History] Nominal GDP at Factor Cost in R NGDP_MP [Display Name] NGDP-MP [History] Nominal GDP at Market prices in NHOUS_FIN_ASSETS [Display Name] Nhous-Fin-Assets [Description] Assumed to have sa NIMPORTS [Description] T128 and Indiastat.com and ES NM1 [Description] T43 [History] Nominal Narrow Money M1 in Rs billio NMM_RATE1 [Display Name] nmm-rate1 [Description] T74--1950-51 to 1970-71 f NRL [Description] T74-Took first no. as 1 yr yield NRS [Description] Annual (Gross) Redemption Yield of Government of Ind NTAX_REV [Display Name] Ntax Rev [History] Nominal Tax revenue (net) in R NW60_100 [Display Name] NW60=100 [History] 1950-51 to 1970-71 spliced wit NWT [History] World Trade $bn Source:IFS NXVOL [Description] T141U and Indiastat.com and ES POP [Description] T1 [History] Population (Million) R_CG_INTEREST_PAYMENT [Display Name] R-CG-interest payment [Description] Modified: 201 R_CG_INTERF [History] Modified: 2013 2018 => -- r_cg_interest_paymentest.fore RAFC04_05 [History] Modified: 1951 2012 // yafc=0 RAFC04_05F [History] Modified: 2013 2018 => -- rafc04_05est.forecast(f=actua RAIN_DEF [Display Name] Rain-Def [History] Rainfall Deficit in %age RAIN_MM [Display Name] Rain-mm [Description] Actual Railfall in Jun-Sep RBANK_CRED [History] Modified: 1951 2012 // rbank_cred=nbank_credit/wpi04_05 RBANK_CRED_0 [Display Name] RBANK_CRED (Baseline) [History] Modified: 2013 20 RBANK_DEPO [History] Modified: 1951 2012 // rbank_depo=nbank_deposit/wpi04_05 RBANK_DEPO_0 [Display Name] RBANK_DEPO (Baseline) [History] Modified: 2013 20


RCONSU [Description] Source: T003A [Remarks] Private Final Consumption RCONSU_0 [Display Name] RCONSU (Baseline) [Description] Source: T003A RCORP_TAX [Display Name] Rcorp Tax [History] Corporation tax in Rs Billio RCORP_TAXF [History] Modified: 2013 2018 => -- rcorp_taxest.forecast(f=actua RDISCRE [Description] T003A [History] Discrepencies in GDP at market pri REER [Description] T149 and Indiastat.com [History] REER Trade weight REER_0 [Display Name] REER (Baseline) [Description] T149 and Indiastat. REG [History] Modified:19512012//reg=rgovt_cons+rgfcf_pub REG_0 [Display Name] REG (Baseline) [History] Modified: 2013 2018 // m REXCISE_TAX [Display Name] Rexcise Tax [History] Excise duties (net) in Rs REXPORT [Description] Exports of Goods and Services Rs bn in 2004-5 prices REXPORT_0 [Display Name] REXPORT (Baseline) [Description] Exports of Goods RFISC_DEF [Display Name] Rfisc def [History] Central govt Gross fiscal def RFOR_ASSETS [Display Name] Rfor-Assets [History] Forex Assets in Rs Billion RFOREX_RES [Display Name] RForex Res [History] Forex Reserves in Rs Billio RGDP_FC [Display Name] RGDP-FC [History] GDP at Factor Cost 2004-5 in Rs RGDP_FC_0 [Display Name] RGDP-FC (Baseline) [History] Modified: 2013 2018 RGDP_MKT_PRICES [Display Name] RGDP-Mkt Prices [History] GDP at Mkt prices Rs B RGFCF [Description] Gross fixed capital formation Rs Bn in 2004-5 prices RGFCF_DSTOCK [Display Name] RGFCF-Dstock [History] Changes in stocks Rs bn in RGFCF_DSTOF [History] Modified: 2013 2018 => -- rgfcf_dstockest.forecast(f=ac RGFCF_PUB [Display Name] RGFCF-Pub [History] Public Sector GDCF in Rs Bn i RGFCF_PVT_F [History] Modified: 2013 2018 => -- rgfcf_pvt_secest.forecast(f=a RGFCF_PVT_SEC [Display Name] RGFCF-Pvt Sec [History] Private Sector GDCF in Rs RGFCFF [History] Modified: 2013 2018 // rgfcfest.forecast(f=actual) rgfcf RGOODS [Description] Accumulated using Private Sector GDCF in Rs Billion RGOODS_0 [Display Name] RGOODS (Baseline) [Description] Accumulated using RGOVT_CONS [Display Name] Rgovt cons [History] Government final consumption RGOVT_CONSF [History] Modified: 2013 2018 => -- rgovt_consest.forecast(f=actu RHOUS_FIN_ASSETS [Display Name] Rhous-Fin-Assets [History] Household Sector net f RIMPORT [Description] T003A [History] mports of Goods and Services Rs bn RIMPORT_0 [Display Name] RIMPORT (Baseline) [Description] T003A RL [Description] RL=NRL-log(P(+5)/P) [History] Real long term inter RM1 [Description] RM1=NM1*100/WPI [History] Real Narrow Money in Rs RM1_0 [Display Name] RM1 (Baseline) [Description] RM1=NM1*100/WPI RS [Description] rs=nrs-log(p/p(-1)) [History] Real short-term inte RSMM [Description] Real short-term interest rate-MM RTAX_REV [Display Name] Rtax Rev [History] Tax revenue (net) in Rs Billio RTAX_REVF [History] Modified: 2013 2018 // rtax_revest.forecast(f=actual) rt RTHETA [Description] Accumulated using Rhous-Fin Assets RTHETA_0 [Display Name] RTHETA (Baseline) [Description] Accumulated using RW0405 [Description] Note 2004-5=100 [History] Wages in PSUs in Rs- per RW0405_0 [Display Name] RW0405 (Baseline) [Description] Note 2004-5=100 RWEALTH [Description] Rwealth=Rtheta+Rgoods [History] Pvt sector Wealth


RWEALTH_0 [Display Name] RWEALTH (Baseline) [Description] Rwealth=Rtheta+R SLR [Description] T46 and CRR-SLR Calc [History] Statutory Liquidity TR [History] Modified: 1951 2012 // tr = @trend TU [Description] Indiastat and ES [History] Number of registered tr US_CPI [Display Name] US CPI [History] US Price index 2004=100 USD_RATE [Display Name] USD Rate [History] Rupees per unit of US Dollar ( WNRL [Description] Source - IFS [History] US Interest Rates, Governme WNRS [Description] Source- IFS [History] US Interest Rates, Governmen WPEXP [Description] Calculated uing the US inflation data WPI04_05 [Display Name] WPI04-05 [History] WPI All Commodities 2004-05=1 WPI04_05F [History] Modified: 2013 2018 // wpi04_05est.forecast(f=actual) wp WPI_0405_MINOIL [Display Name] WPI-0405-MinOil [History] WPI Mineral Oil 2004-5= WPI_MIN_OIL [Display Name] WPI-Min Oil [History] WPI Mineral Oil 1970-71=100 WPOP [Description] calculated using population and decadel growth in ol WPOPF [History] Modified: 2013 2018 // wpopest.forecast(f=actual) wpopf WT_2004 [Display Name] WT-2004 [History] World Trade WT_2004F [History] Modified: 2013 2018 // wt_2004est.forecast(f=actual) wt_ XVOL [Description] xvol=rexport-rimport [History] In Rs bn in 2004-5

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APPENDIX 5 Forecast of exogenous variables In case of other variables showing constant growth, simple time series models were estimated. These models were used to forecast the values of the exogenous variables over the forecast period and stored as the EViews 7 series of the variable itself respectively. A. Estimation of the Corporation Tax (RCORP_TAX) Model using LS Method

Dependent Variable: LOG(RCORP_TAX) Method: Least Squares Sample (adjusted): 1952 2012 Included observations: 61 after adjustments


C 0.038394 0.059662 0.643529 0.5224 LOG(RCORP_TAX(-1)) 1.008829 0.012557 80.33921 0.0000


Graph of the RCORP_TAX Model and its Residuals


B. Estimation of the Adjustment to Factor Cost (RAFC04_05) Model using LS Method

Dependent Variable: LOG(RAFC04_05) Method: Least Squares Sample (adjusted): 1952 2012 Included observations: 61 after adjustments


C 0.116696 0.085667 1.362210 0.1783 LOG(RAFC04_05(-1)) 0.990630 0.012757 77.65397 0.0000


Graph of the RAFC04_05 Model and its Residuals

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C. Estimation of the RGFCF Stock Adjustment (RGFCF_DSTOCK) Model using LS Method

Dependent Variable: RGFCF_DSTOCK Method: Least Squares Sample (adjusted): 1952 2012 Included observations: 61 after adjustments


C -72.37305 64.26197 -1.126219 0.2647 RGFCF_DSTOCK(-1) 0.859254 0.090388 9.506274 0.0000 @TREND 4.415337 2.150789 2.052892 0.0446

R-squared 0.759382 Mean dependent var 266.5261 Adjusted R-squared 0.751084 S.D. dependent var 475.7132 S.E. of regression 237.3401 Akaike info criterion 13.82480 Sum squared resid 3267160. Schwarz criterion 13.92861 Log likelihood -418.6563 Hannan-Quinn criter. 13.86548 F-statistic 91.52280 Durbin-Watson stat 2.482971 Prob(F-statistic) 0.000000

Graph of the RGFCF_DSTOCK Model and its Residuals


D. Estimation of the RGFCF of Private sector (RGFCF_PVT_SEC) Model using LS Method

Dependent Variable: LOG(RGFCF_PVT_SEC) Method: Least Squares Sample (adjusted): 1952 2012 Included observations: 61 after adjustments


C 0.584704 0.315238 1.854802 0.0687 LOG(RGFCF_PVT_SEC(-1)) 0.892410 0.058589 15.23166 0.0000 @TREND 0.008146 0.003664 2.223389 0.0301


Graph of the RGFCF_PVT_SEC Model and its Residuals


E. Estimation of the Government Consumption (RGOVT_CONS) Model using LS Method �

Dependent Variable: LOG(RGOVT_CONS) Method: Least Squares Sample (adjusted): 1952 2012 Included observations: 61 after adjustments


C 0.039246 0.038584 1.017170 0.3132 LOG(RGOVT_CONS(-1)) 1.002765 0.005552 180.5989 0.0000


Graph of the RGOVT_CONS Model and its Residuals

1. Now, we have exogenous variables data over the forecast horizon. EViews 7 model’s dynamic solution package is used to forecast the values of the endogenous variables. The results were examined graphically. As no strange behavior was observed in any variable forecast, we did not use add factors as of now.


Dynamic Solution of the Model over the Forecast Period 2013 to 2018

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Capital Requirements for Indian Banks An Empirical Analysis

This paper attempts to explain and analyze the capital requirements of Indian banks given the exposure to the infrastructure sector in the context of the estimated $1 trillion infrastructure investment requirement from 2013 to 2018. It also addresses the implications of this proposed expansion in infrastructure lending, given that banks in India would be capital-constrained through implementation of Basel III requirements and emerging risks from currency depreciation and slow growth in gross domestic product.

About the Asian Development Bank

ADB’s vision is an Asia and Pacific region free of poverty. Its mission is to help its developing member countries reduce poverty and improve the quality of life of their people. Despite the region’s many successes, it remains home to approximately two-thirds of the world’s poor: 1.6 billion people who live on less than $2 a day, with 733 million struggling on less than $1.25 a day. ADB is committed to reducing poverty through inclusive economic growth, environmentally sustainable growth, and regional integration.

Based in Manila, ADB is owned by 67 members, including 48 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance.

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CAPITAL REQUIREMENTS FOR INDIAN BANKS: AN EMPIRICAL ANALYSIS · Capital Requirements for Indian Banks: An Empirical Analysis 3 top 20 borrowers from banks, which included large infrastructure