Secondary report SAAD Group

Impact of Import & Exchange Rate on Foreign Direct Investment

Time series Evidence from Bangladesh

A research project submitted

By

Hafiz Shahzad Siddiqui (9610)

[email protected]

Saqib Rasheed (9563)

[email protected]

Syed Muhammad Saad (9629)

[email protected]

To

Faculty of Masters in Business Administration

Mr. Tehseen Javaid

FALL 2016

Acknowledgement

‘In the Name of Allah, the most Beneficent, the most Merciful’

This report reflects our efforts over a number of weeks. First of all, we would like to

thank our facilitator and guide Mr. Tehseen Jawaid for giving us a chance to take this

opportunity, and also for his guidance and encouragement through the project of

Research Methods; His help and wide knowledge is blessing for us, and his guidance on

each stage of this report give us encouragement to do hard work for the report what was

beyond our limits and Al-Hamdulillah we get the desired out comes.

At last, but most important, we thank almighty Allah, our parents and fellows for their

support and encouragement. Without their blessings this report would be impossible.

Abstract

This research is aimed to study the relationship among foreign direct investment, merchandise

import and exchange rate in Bangladesh. The study analyzed the time series data and panel data

both, of all relevant variables from the year 1980 to 2013, data have been collected from World

Bank. Foreign direct investment (FDI) is used as dependent variable, where Merchandise import

(MI) and Exchange rate (ER) are used as independent variables. Stationary analysis, Ordinary

least square (OLS) method, Co-integration test, and Granger causality test techniques have been

applied to identify relation and impact among foreign direct investment, merchandise import and

exchange rate. It is found that there is a negative relationship between exchange rate and foreign

direct investment, as well as there is positive relationship between import and foreign direct

investment. Granger-type causality test shows that there is unidirectional causality present in the

model between Merchandise Import and Foreign direct investment, it also shows that there is no

causal relationship between Exchange Rate and Foreign direct investment.

It is recommended that policy makers should make more market-oriented policy changes in

Bangladesh to create a broadminded environment for foreign trade.

Key Words: Import, Exchange Rate, Foreign Direct Investment.

Table of Contents

1. INTRODUCTION: .................................................................................................................. 1

2. LITERATURE REVIEW: ....................................................................................................... 4

2.1. THEORETICAL

BACKGROUND:……………………………………………………………..ERROR! BOOKMARK

NOT DEFINED.

2.2. EMPIRICAL STUDIES:………………………………………………....................................5

3. METHODOLOGY: ............................................................................................................... 10

4. DATA ANALYSIS: .............................................................................................................. 11

TIME SERIES DATA ANALYSIS:

……………………………………………………...ERROR! BOOKMARK NOT DEFINED.

4.1. STATIONARY TEST:…………………………………………………………………….....11

4.2. ORDINARY LEAST SQUARE METHOD:…………………………………………………….12

4.3. BREUSCH-GODFREY SERIAL CORRELATION LM TEST:…………………………………...13

4.4. REMOVAL OF AUTOCORRELATION:…………………………………………………...…..14

4.5. CO-INTEGRATION TEST:…………………………………………………………………..15

4.6. MEASURE OF MULTICOLINEARITY

(VIF):………………………………………………...1ERROR! BOOKMARK NOT DEFINED.

4.7. HETEROSKEDASTICITY TEST:……………………………………………………………..17

4.8. STABILITY ANALYSIS:………………………………………………………………….…18

4.9. CHOW BREAK POINT TEST:………………………………………………………….……19

4.10. CAUSALITY ANALYSIS:……………………………………………………………….…..19

PANEL DATA ANALYSIS:……………………………………………………….……20

4.12. STATIONARY TEST:………………………………………………………………….……20

4.13. POOLED ORDINARY LEAST SQUARE METHOD:…………………………………………...21

4.14. CO-INTEGRATION TEST:………………………………………………………………..…22

5. CONCLUSION AND POLICY IMPLICATION: ................................................................ 22

6. REFERENCES: ..................................................................................................................... 24

7. APPENDIX - A: .................................................................................................................... 26

8. APPENDIX - B: .................................................................................................................... 56



1

1. INTRODUCTION

Many authors and researcher or scholars have focused on the impact of exchange rate on

foreign direct investment .1 On the topic impact of exchange rate on foreign direct investment in

all over the world in most of the studies panel data is used.2 There are very few studies have been

conducted to examine the Inter-Linkage between Foreign Direct Investment, Imports and

Exchange Rate. 3 But according to our knowledge there is no study that has been conducted to

examine the impact of import on foreign direct investment. It is cleared that there are so many

researcher who focused on impact of exchange rate on foreign direct investment but after

reviews several literatures we found that in Bangladesh no one conceder impact of import &

exchange rate on foreign direct investment; Thus, in this study we have tried to examine the

impact of import and exchange rate both on foreign direct investment because there is a need of

research work in this context to know the impact of import & exchange rate on foreign direct

investment.

Keeping in view of all the previous points it can be said that Foreign Direct Investment is a most

significant indicator in order to analyze the economic system. It has observed that Foreign Direct

Investment of any country is affected by macroeconomic elements like exchange rate, inflation,

interest rate, economic growth and so on; so there are so many independent variables which

affect the FDI of a country; import and exchange rate are two of them. Hence Foreign Direct

Investment has been taken as dependent variable for this study, where exchange rate and import

are our independent variables.

1 (Hafeez-ur-Rehman, Atif Ali Jaffri & Imtiaz Ahmed, 2010) 2 (Dr. VB Khandarei, 2016) 3 (Muhammad Abdul Kamal, Zhaohua Li, Sara Rafiq, Amna Nazeer, Khalid Khan, Khuram Shafi, 2014)



2

In this study we have taken the time series data and panel data of 33 years (from 1980 to 2013)

of merchandise import, exchange rate and foreign direct investment of Bangladesh and India.

Bangladesh has been use as the entity for the time series data, where Bangladesh and India are

used as entities for the panel data. Website of World Bank has been used as the source of data.

Before proceed further let’s view to the variables:

1) FDI (Foreign direct investment)

Foreign direct investment or FDI is an investment in a business of one country made

by an individual or a firm (investor) from another country. Foreign direct investment

has proved itself to be flexible during financial crises; such investment was amazingly

stable during the global financial crises of 1997-1998.4

2) ER (Exchange Rate)

An exchange rate is a price of currency of one country in terms of another currency,

where the national currency is valued in relative to foreign currency; therefore the

exchange rate has two components; one is the domestic currency and second is

foreign currency.

3) IMP (Import)

An import can be good or service brought from a country to another country, so the

word import is taken from the word port, because goods are frequently shipped

through airplanes, ships or boats to other countries.

The word merchandise import means import of the goods which are tangible.

4 (Prakash Loungani and Assaf Razin, 2001)

http://www.investopedia.com/terms/c/currency.asp

http://www.imf.org/external/pubs/ft/fandd/2001/06/loungani.htm#author



3

The relationship between the foreign direct investment, merchandise import and exchange rate of

Bangladesh5 is as shown in the graph 1.1.

Graph 1.1

Relationship between FDI, Import and Exchange rate

This study is organized as following manner: Section 2 reviews the theoretical background and

empirical literature on the relationship foreign direct investment, import and exchange rate. In

section 3 methodology has been discussed for empirical investigation of the relationship. In

section 4 data analysis is showing the estimation results of tests. And section 5 is the final

section which concludes the whole study and it also provides the policy implications.

5 (Data source: World Bank)

-10

0

10

20

30

40

50

60

70

80

90

FDI

IMP

ER



4

2. LITERATURE REVIEW

2.1 Theoretical Background

On the basis of literature reviews and studies the framework constructed the effect or impact of

import and exchange rate (which are independent variables) on the foreign direct investment or

FDI (which is dependent variable).

Fig 2.1 Schematic Diagram of Variables

It is observed that there are many theories have been proposed the effect or relation of exchange

rate (ER) on foreign direct investment (FDI); The central focus of our report is to examine the

relationship between the exchange rate (ER) as well as import (IMP) and foreign direct

investment (FDI), and the possible interdependence of FDI over these variables, which is

described in the model: Markov zero-inflated Poisson (MZIP), it is developed by Wang (2001) 6.

Through the entire literature review we are assuming the following hypotheses.

6 (Peiming Wang ,2001)

FDI

Import Exchange Rate



5

Ho: There is a positive relationship between foreign direct investment, import and exchange rate.

2.2 Empirical Studies

Seo, Tarumun and Suh (2002) 7 examine that is there any impact of exchange rates on the

FDI (foreign direct investment) inflows in Asia, by using the data from 1985 to 2000.

Independent variables have been used FX (real exchange rate between local currency i.e. Korean

Won and US dollar), VOL (measures volatility of exchange rate), SHAREP (share price index)

and WAGE (real wage index), where FDI (foreign direct investment) has been used as dependent

variable. Techniques have been applied, results show that ER or exchange rate level was

consistently positive influence of FDI or foreign direct investment flows.

Xing & Zhao (2003) 8 investigate the systematic linkage between Foreign Direct

Investment (FDI), Reverse Imports (IMP) and the Exchange Rates (ER); by using the source of

data China Statistic Yearbook, International Financial Statistic & IMF from the year 1994 to

2001.Variables have been used: Foreign Direct Investment (FDI), Reverse Imports and

Exchange Rates (ER); Basic model setup techniques were applied. Results show that exchange

rate affects Foreign Direct Investment and Imports.

Khan and Nawaz (2010) 9 investigate the determinants of foreign direct investment in

Pakistan. using annual data for the period from 1970-71 to 2004-05, by using variables of (FDI,

GDP, EXR, EXP, TAR, WPI) Where FDI is Annual Foreign Direct Investment in Dollars, GDP

7 (Jing-Soo Seo, Suwardi Tarumun & Chung-Sok Suh, 2002) 8 (Yuqing Xing & Laixun Zhao, 2003) 9 (Rana Ejaz Ali Khan and Muhammad Atif Nawaz, 2010)



6

is Annual growth rate of GDP, EXR is Annual average exchange rate as Rupees/Dollar, EXP is

Exports of goods and services from Pakistan, TAR is the Custom duty on imports in the country,

and WPI is the General wholesale price index of the country. The techniques which are used in

this research are OLS model and Regression Analysis. The regression results confirmed that an

increase in GDP growth rate has positive effect on inflow of FDI in Pakistan. Hence the

authorities should positively concentrate on maximum utilization of resources to increase GDP

growth rate. The important finding of the study is that export demand that is shown by the bulk

of exports is major determinant of FDI in Pakistan. It is recommended that the government

should make a paradigm shift in its investment policy to attract FDI. It should focus on export-

oriented industries instead of encouraging FDI for domestic consumption. The national trade

policy should focus on exports by increasing export processing zones, global market orientation

and adjusting fiscal policies. A co-efficient of import tariff suggested an important role of the

government in promoting the foreign investment in the country. It needs effective and

encouraging import policies from the public sector to restore the confidence of the investors.

Rehman, Jaffri & Ahmed (2010)10 examine the impact of Foreign Direct Investment

(FDI) and worker remittances on equilibrium real exchange rate of Pakistan; By using data from

1993 to 2009 and the source of data used State Bank of Pakistan. Variables have been used are

Foreign Direct Investment (FDI), Behavioral Equilibrium Real Exchange Rate (BEER), Real

Exchange Rate Misalignment (RERM), Equilibrium Real Exchange Rate (ERER), GDP and

Fiscal Year. Co-integration techniques have been applied; and the results show that Foreign

Direct Investment (FDI) Inflows and remittance of workers support the real exchange rate (in

Pakistan). There are some limitations in this study that monthly data on variables were absent, so

10 (Hafeez-ur-Rehman, Atif Ali Jaffri & Imtiaz Ahmed, 2010)



7

it is recommended that in future this type of research should explore variation in estimation

techniques.

Omankhanlen (2011)11 investigate that effect of inflation and exchange rate and the

bidirectional influences between FDI and economic growth in Nigeria. By using time series data

Inflation, Exchange rate, foreign direct investment variable have been created. A linear

regression analysis was used on the thirty year data to determine the relationship between

inflation, exchange rate, FDI inflows and economic growth. Results show that FDI follow

economic growth occasioned by trade openness which saw the entry of some major companies

especially the telecommunication companies, while Inflation has no effect on FDI. However

exchange rate has effect on FDI. It is recommended that The nation’s monetary authorities

should develop and implement measures that will ensure that both inflation and foreign exchange

rates are sustained at levels that will ensure increasing level of inflow of FDI.

Jin and Zang (2013)12 investigate that the impact of changes in exchange rate in the host

country on FDI, with reference to international and domestic research. By using time series data

foreign direct investment (FDI) & effective exchange rate (REER) variables have been created.

The empirical test used. Results showed that the appreciation of RMB promotes FDI after the

reforms in the exchange rate regime in 2005 and this phenomenon is a result from the change in

the type of FDI into China in recent years. It is recommended that FDI into China in recent years.

In the long term, the proper appreciation of RMB and a more flexible exchange rate regime will

impact on China's currency and micro-control policies positively.

11 (Alex Ehimare Omankhanlen,2011) 12 (Weifeng JIN & Qing Zang, 2013)



8

Emmanuel (2013) 13 investigates the effect of FDI on economic growth in Nigeria on the

basis of selected macro economic variables of GDP, inflation and Exchange Rate, by using

annual data for the period from 1986 to 2011, by using variables of (GDP, EXR, INFL) where

GDP = Gross Domestic Product used as proxy for Economic Growth with 1 year lag, EXR =

Nominal Exchange Rate of Monthly Average Rate, INFL = Inflation Rates. The techniques

which are used in this research are Ordinary Least Squares (OLS) and Multiple Regression

analysis. Ordinary Least Squares (OLS) is used to examine the relationship between the

Dependent variable (FDI) and the independent variables –Inflation and Exchange Rate. The

study indicates that GDP, inflation and Exchange Rate are affected to the extent of 46.5% by

FDI. FDI does not make the GDP to grow, increases inflation and has negative effect on

exchange rate. The result showing the relationship between FDI and GDP does not follow

theoretical and prior expectations where inflows of FDI should have positive and significance

influence on GDP. It is suggested that the government should create enabling environment for

FDI to operate. Also FDI should not come in short term (hot money) investment that encourages

capital flight. Nigeria is still smarting from the enormous flight from investment in stock market

which almost closed the Nigerian Stock Exchange

Bilawal, Ibrahim, Abbas, Shuaib, Ahmed, Hussain & Fatima (2014) 14 investigate that

whether uncertainty or fluctuations in exchange rate affects the Foreign Direct Investment in

Pakistan. This study is based on the time series data from the year of 1982 to 2013 & the source

of the data was the website of the State Bank of Pakistan. Variables were used: Foreign Direct

Investment (as a dependent variable) and Exchange Rate (as an indirect variable); Techniques of

13 (Umeora Chinweobo Emmanuel, 2013) 14 (Muhammad Bilawal, Muhammad Ibrahim, Amjad Abbas, Muhammad Shuaib, Mansoor Ahmed, Iltaf Hussain,

Tehreem Fatima,2014)



9

correlation and regression analysis has been used. The result shows that there is a positive

significant relationship among Exchange Rate and Foreign Direct Investment; it has been

observed that Exchange Rate (independent variable) has 67% impact on Foreign Direct

Investment (dependent variable).

Kamal, Li, Rafiq, Nazeer, Khan, Shafi (2014) 15 investigates empirically the relationship

between FDI and import in Pakistan using panel data for the period from 1980 to 2012, by using

variables of (FDI,M,ER). Where FDI is Foreign Direct Investment, M is imports and ER is

exchange rate. The techniques which are used in this research are vector error correction model

(VECM) and linear hypothesis testing, ADF stationary test, Johansen co-integration test. The

vector error correction model (VECM) and linear hypothesis testing have been applied by

considering exchange rate as supplement for better and accurate modeling. In this study, co-

integration and error-correction techniques were used to find whether there was substitutability

or complementary between FDI and imports in Pakistan. The results favors complementary

hypothesis as FDI causes rise in foreign imports. Further, it is also conclude that FDI is not

directed towards export oriented production activates or FDI is contributing meagerly to the

exports of Pakistan. The results indicated short run as well as highly significant long run

relationship among all variables under study. For imports causality runs from FDI to imports

indicating FDI to be complementary variable for imports. It is recommended that the

Government needs to implement those FDI policies which stimulate exports but reduce the

imports burden; in order to generate employment and to reduce the balance of payments

problems, it is suggested that government should encourage FDI policies relevant to export

oriented industries like manufacturing and production sectors along with the exploration of

15 (Muhammad Abdul Kamal, Zhaohua Li, Sara Rafiq, Amna Nazeer, Khalid Khan, Khuram Shafi, 2014)



10

natural resources. Policies should aim to encourage FDI in industries where in the surge in

import bill is compensated with better exports performance of the firms.

Khandare (2016)16 examines the impact of Exchange Rate on Foreign Direct Investment

in India and China, by using the panel data from 1991 to 2014. Variables have been used:

Exchange Rate as an independent variable and Foreign Direct Investment as a dependent

variable; the correlation and regression analysis techniques have been applied. Results show that

there is a positive correlation between Foreign Direct Investment and Exchange Rate is negative,

it has been found that 1 unit increase in Exchange Rate raises Foreign Direct Investment by

0.605 units in India, where in the case of China 1 unit increase in Exchange Rate leads to

decrease of Foreign Direct Investment by 0.605.

3. METHODOLOGY

According to the empirical studies and our observations the equation for Foreign Direct

Investment (FDI) is depends on Import (IMP) and Exchange Rate (ER). To examine the

impact of import and exchange rate on foreign direct investment following equation has

been used.

FDI = α + β1 (IMP) + β2 (ER) + e

16 (Dr. VB Khandare, 2016)



11

In this equation, FDI is representing foreign direct investment which has positive relationship

with IMP and ER; where IMP represents the import, and ER represents the exchange rate. All

data sets used in this study from 1980 to 2013 are taken from World Bank’s website.

4. DATA ANALYSIS

TIME SERIES DATA ANALYSIS

4.1 Stationary Test:

In time series analysis, the unit root test has its own importance to avoid from the unit root

problem which results in the spurious relationship between dependent and independent variables.

It is also necessary to select an appropriate estimation technique for estimating the econometric

models. For this purpose, the Augmented Dickey Fuller (ADF) unit root test was used on both at

level and first differences for all variables to be used in the model. Two different models have

been considered while performing tests. The model with constant (C) assumes that there are no

trends in the levels of the data, such that difference series have zero mean. While the model with

a constant (C) and linear trend (T) is used when linear trends in the levels of the data are

observed. The results of the unit root tests are reported in table 4.1.



12

Table 4.1: Unit root test

Variables ADF test statistics

I(0) I(1)

C C &T C C&T

FDI 1.0000 1.0000 0.0007 0.0002

IMP 1 0.9924 0.0001 0.0001

ER 0.9454 0.2857 0.0000 0.0002

Ho: Series is not stationary.

H1: Series is stationary.

In the above table that it is concluded that all the variables found non-stationary at level I (0)

greater than 0.1 and become stationary at the first difference I(1). The test results confirm that

trend exists at level and trend does not exist at first difference.

After applying stationary test we run “OLS” by using the variables of FDI, IMP and ER.

F.D.I= (IMP) + (ER) + e

4.2 Ordinary Least Square Method:

OLS is a method for estimating the unknown parameters in a linear regression model, and it is

used to minimize an error to find the trend line equation.

Table 4.2

Variable Coefficient Std. Error t-Statistic Prob.

C -3.00E+07 1.25E+08 -0.23898 0.8127

IMP 0.065441 0.006942 9.426316 0.00

ER -6367013 3857701 -1.65047 0.1089

R-squared 0.904487 Durbin-Watson

stat 1.449059 F-statistic 146.7824

Prob(F-statistic) 0.000



13

The result of OLS shows that there is a significant impact of Import (IMP) on F.D.I because

Prob. value of import is 0.000 which is less than 0.1, but there is no significant impact of E.R on

F.D.I because Prob. value of E.R is 0.1089 > 0.1. one unit increase in IMP will increase F.D.I by

0.0654 units while 1 unit increase in E.R will decrease F.D.I by 6367013 units, IMP have

positive impact and E.R have negative impact on F.D.I other things remains constant. It can be

expressed as:

F.D.I= -3E+07 + 0.065441 (MI) -6367013 (ER) + e

The value of R-squared in the table is 0.90 which shows that our model capture 90% deviation, it

means that 90% IMP and E.R explains F.D.I. The prob. Value of F-statistic shows an overall

impact and significance of the model, in this table prob. Value of F-statistic is 0.000 which is less

than 0.1 so we can say that the combine effect of independent variables is significant. The value

of Durbin-Watson stat is 1.449059 which tells that there is a chance of auto-correlation in the

model and hence the value is < 2 which means that there is a positive auto-correlation in the

model.

4.3 Breusch-Godfrey Serial Correlation LM Test:

Auto-correlation in the model is tested by Breusch-Godfrey Serial Correlation LM Test.

HO: No auto-correlation.

H1: Auto-correlation is present.



14

Table 4.3

Breusch-Godfrey Serial

Correlation LM Test:

F-statistic 88.13254 Prob. F(1,42) 0.000

The Prob. value of F-statistic in the above table is 0.000 which is less than 0.1, so we reject Ho.

which means that auto-correlation is present in the model.

4.4 Removal of autocorrelation:

To remove auto-correlation from the model we change measure of variable from import to

merchandise import, after changing measure of variable (merchandise import), the auto-

correlation removes from the model.

Table 4.4

Breusch-Godfrey Serial

Correlation LM Test:


The Prob. value of F-statistic in the above table is 0.6257 which is greater than 0.1, so we

accept null hypothesis, which means that there is no auto-correlation in the model.



15

4.5 Co-integration Test:

After test the auto-correlation in the model we check that whether the strong relationship is

present in the model or not because the combination of one or more of these series may exhibit a

long run relationship. It was confirmed through co-integration test using Johansen Co-integration

Method. Johansen and Juselius (1990) have derived two tests for co-integration, namely, the

Trace test and the Maximum Eigen value test. The Claim is that there is no co-integration in

data.

Ho: No Co-Integration.

H1: There is a co-Integration.

Johansen Co-integration test, Lag 2 and Model 3 ( Linear deterministic trend) has been applied,

The summary of Johansen Co-integration and their corresponding critical values are presented in

Table 4.5.

Table 4.5 (a)

Trace:

Unrestricted Cointegration Rank Test (Trace) Hypothesized

Trace 0.1

No. of CE(s) Eigen value Statistic Critical Value Prob.**

None * 0.877368 72.4078 27.06695 0

At most 1 0.205143 7.352203 13.42878 0.537

At most 2 0.007546 0.234815 2.705545 0.628



16

Table 4.5 (b)

Maximum Eigen value:

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.1

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.877368 65.0556 18.89282 0

At most 1 0.205143 7.117388 12.29652 0.475

At most 2 0.007546 0.234815 2.705545 0.628

In the above table of co-integration (trace) At most 1 and 2 Trace is less than critical value (0.1)

so we accept the null hypothesis of no co-integration, similarly in table of co-integration

(Maximum Eigen value) At most 1 and 2 Max-Eigen is less than critical value at 10% level of

significance so we accept the null hypothesis and said that there is no co-integrating vector is

present in the model.

4.6 Measure of Multicolinearity (V.I.F):

Multicolinearity occurs when independent variables co-relate each other in the model and it is

measured by V.I.F.

HO: There is no co-relation.

H1: There is a co-relation.



17

Table 4.6

Variance Inflation Factors

Coefficient Uncentered Centered

Variable Variance VIF VIF

C 1.57E+16 14.40111 NA

IMP 4.82E-05 9.581438 4.58209

ER 1.49E+13 34.67286 4.58209

In the above table the centered V.I.F value of both independent variables are less than 10 which

is acceptable so we can said that there is no issue of Multicolinearity in the model and accept the

null hypothesis.

4.7 Heteroskedasticity Test

“In statistics, when the standard deviations of a variable, monitored over a specific amount of

time, are non-constant. Heteroskedasticity occurs. This problem often arises in cross sectional

data and not in time series data. Here the assumption is that variance of error is constant. White

test is run to check the presence of heteroskedasticity in the model. The null hypothesis states

that there is no heteroskedasticity.

Ho : No Heteroskedasticity.

H1 : Heteroskedasticity is present.

Table 4.7

Heteroskedasticity

Test: White

F-statistic 25.69227 Prob. F(5,28) 0



18

The above table shows the Prob.F value is less than 0.1 therefore null hypotheses is rejected

concluding that there is a heteroskedasticity present in the model.”

4.8 Stability Analysis:

Stability analysis is used to check the consistency of data through CUSUM and CUSUM of

Squares test as shown in the graph 4.8.

Graph 4.8

CUSUM and CUSUM of Squares test.

In CUSUM test the results within 2 standard deviations but CUSUM of Squares test shows

fluctuation in 2012 and outsides the 2 standard deviations so; we can confirm this through chow

breakpoint test as shown in the table 4.6

-20

-15

-10

-5

0

5

10

15

20

84 86 88 90 92 94 96 98 00 02 04 06 08 10 12

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

84 86 88 90 92 94 96 98 00 02 04 06 08 10 12

CUSUM of Squares 5% Significance



19

4.9 Chow Break Point Test:

The CUSUM & CUSUM square results are opposite to each other then we have to check our

model’s stability with “Chow Breakpoint Test”. Chow breakpoint tests checks the consistency of

β and stability of the model.

Ho: Parameters are stable.

H1: Parameters are not stable.

Table 4.9

F-statistic (2011) 28.74233 Prob. F(3,40) 0.000

In the above table the prob. Value of F-statistic of 2012 year in chow breakpoint test is 0 less

than 0.1 and we can say that parameters are not stable so we can reject the null hypothesis. And

therefore the overall model is unstable.

4.10 Causality Analysis:

Granger causality analysis is used to find the cause of the relationship among the focus variables

(FDI, IMP, and ER) which cannot be determined through regression analysis. The results of Granger

causality are reported in Table 4.10.

TABLE 4.10

Null Hypothesis: F-Statistic Prob.

IMP does not Granger Cause FDI 9.57149 0.0043

FDI does not Granger Cause IMP 0.10728 0.7455

ER does not Granger Cause FDI 1.84329 0.1847

FDI does not Granger Cause ER 0.4007 0.5315

ER does not Granger Cause IMP 2.6585 0.1135

IMP does not Granger Cause ER 1.62268 0.2125



20

As shown in the above table unidirectional causality present in the model between IMP and FDI

because IMP does granger cause on FDI but FDI does not granger cause on IMP, so we reject

our null hypothesis. Similarly there is no causal relationship between ER and FDI, and between

FDI and ER because prob. Value is greater than 0.1, so we accept the null hypothesis. Similarly

there is no causal relationship exists between ER and IMP and between IMP and ER, so we

accept the null hypothesis.

PANEL DATA ANALYSIS

For panel data testing we considered a data of two countries Bangladesh and India from 1980

to 2013.

4.11 Stationary Test:

Variables ADF test statistics

I(0) I(1)

C C &T C C&T

FDI 0.9914 0.9370 0.0102 0.0056

IMP 1.0000 0.9999 0.0012 0.0000

ER 0.986 0.3057 0.0000 0.0003

In the above table of unit root test it is In the above table it is concluded that all the variables

found non-stationary at level I (0) greater than 0.1 and become stationary at the first difference

I(1). After applying stationary test we run „OLS‟ by using the variables of FDI, IMP and ER, the

test results confirm that trend exists in the data.



21

4.12 Ordinary Least Square Method:

Table 4.12


C -3.58E+08 1.12E+09 -0.320588 0.7496

IMP 0.079876 0.004295 18.59866 0.000

ER -729152 26146303 -0.027887 0.9778

R-squared 0.848021 Durbin-Watson stat 0.752109

F-statistic 181.3449 Prob(F-statistic)

0.000

The result of OLS shows that there is a significant impact of IMP on F.D.I of Bangladesh and

India because Prob. value of import is 0.000 which is less than 0.1, but there is no significant

impact of E.R on F.D.I of Bangladesh and India because Prob. value of E.R is 0.9778 <0.1. one

unit increase in IMP will increase F.D.I by 0.079876 units while 1 unit increase in E.R will

decrease F.D.I by 729152 units, IMP have positive impact and E.R have negative impact on

dependent (F.D.I) other things remains constant. It can be expressed as:

F.D.I= -3.58E+08 + 0.079876 (IMP) -729152 (ER) + e

The value of R-squared in the table is 0.84 which shows that our model capture 84% deviation, it

means that 84% IMP and E.R explains F.D.I. The prob. Value of F-statistic shows an overall

impact and significance of the model, in this table prob. Value of F-statistic is 0 which is less

than 0.1 so we can say that the combine effect of independent variables is significant.



22

4.13 Co-Integration Test:

Unrestricted Cointegration Rank Test (Trace and Maximum Eigenvalue)

Hypothesized Fisher Stat.* Fisher Stat.*

No. of CE(s) (from trace test) Prob. (from max-eigen test) Prob.

None 26.24 0 24.52 0.0001

At most 1 7.378 0.12 8.396 0.0781

At most 2 0.804 0.94 0.804 0.9379

In the above table of co-integration (Trace and Maximum Eigenvalue) At most 1 and 2 in fisher test

from (trace test) the prob. Value is greater than (0.1) so we reject the null hypothesis of no co-

integration, similarly in fisher test from (Maximum Eigen test) At most 1 the prob. value is less

than 0.1 but at most 2 greater than 0.1 so we reject the null hypothesis and said that there is a co-

integrating vector is present in the model.

5. CONCLUSION AND POLICY IMPLICATION

This study aims at investigating the impact of import and exchange rate on foreign direct

investment in Bangladesh using time series data from 1980-2013. To find the relationship

between these variable O.L.S method, and co-integration test, Granger causality test were used.

The most important finding suggests that there is a significant and positive impact of import on

F.D.I while negative and insignificant impact of exchange rate on F.D.I. From the co-integration

test it is found that there is no co-integrating vector is present in the model, hence is no strong



23

and long run relationship present between dependent and independent variables. It is also found

that from Causality Analysis there is a unidirectional causality present in the model between

merchandise import and FDI. Merchandise import has a causal effect on FDI of Bangladesh. By

using panel data of Bangladesh and India it is investigated that there is a significant and positive

impact of Import on F.D.I of Bangladesh and India but there is insignificant impact and negative

impact of Exchange rate on F.D.I of Bangladesh and India, which means that increase in import

will decreases the F.D.I and similarly increases in exchange rate will decreases the F.D.I in all

countries around the world.

5.1 Policy implication

As the results of tests mentioned earlier: if import increases than FDI also increases, and if

exchange rate increases than FDI decreases. It is suggested that policy makers should notice the

impact of the exchange rate and import on FDI in the implementation of trade policies of

Bangladesh. So policy makers should make more market-oriented policy changes in Bangladesh

to create a broadminded environment for foreign trade and foreign direct investment. Policy

makers of Bangladesh should work on decreasing Exchange rate for increasing the foreign direct

investment, as interest rates and exchange rates are all highly correlated and Exchange rate can

be decreased by manipulating interest rates, it will increase the value of local currency which

attracts foreign capital.



24

REFERENCES

Bilawal, M., Ibrahim, M., Abbas, A., Shuaib, M., Ahmed, M., Hussain, I., & Fatima, T. (2014).

Impact of Exchange Rate on Foreign Direct Investment in Pakistan. Advances in

Economics and Business, 2(6), 223-231.

Jaffri, A. A., & Ahmed, I. (2010). Impact of foreign direct investment (FDI) inflows on

equilibrium real exchange rate of Pakistan. South Asian Studies, 25(1), 125.

JIN, W., & ZANG, Q. (2013). Impact of change in exchange rate on foreign direct investment:

Evidence from China. Lingnan Journal of Banking, Finance and Economics, 4(1), 1.

Kamal, M. A., Li, Z., Rafiq, S., Nazeer, A., Khan, K., & Shafi, K. Inter-Linkage between FDI,

Imports and Exchange Rate: An Empirical Evidence from Pakistan.

Khan, R. E. A., & Nawaz, M. A. (2010). Economic determinants of Foreign direct investment in

Pakistan. Journal of Economics, 1(2), 99-104.

Khandare, V. B. (2016). Impact of exchange rate on FDI: A comparative study of India and

China. IJAR, 2(3), 599-602.

Loungani, P., & Razin, A. (2001). How beneficial is foreign direct investment for developing

countries?. Finance and Development, 38(2), 6-9.

Omankhanlen, A. E. (2011). The effect of exchange rate and inflation on foreign direct

investment and its relationship with economic growth in Nigeria. EA1, 1.

Seo, J. S., Tarumun, S., & Suh, C. S. (2002, July). Do Exchange Rates Have any Impact on

Foreign Direct Investment Flows in Asia: Experiences of Korea. In first annual



25

conference, Korea and the World Economy (Seoul, Korea Yonsei University, 21–22 July

2002) (pp. 1-18).

Umeora, C. E. (2013). Effects of Foreign Direct Investment (FDI) on Economic Growth in

Nigeria. Available at SSRN 2285329.

Wang, P. (2001). Markov zero-inflated Poisson regression models for a time series of counts

with excess zeros. Journal of Applied Statistics, 28(5), 623-632.

Xing, Y., & Zhao, L. (2003). Reverse Imports, Foreign Direct Investment and Exchange

Rates (No. EMS_2003_03



26

APPENDIX -A

TIME SERIES OUTPUT RESULTS

1. OLS REGRESSION

Dependent Variable: FDI

Method: Least Squares

Date: 10/31/16 Time: 08:00

Sample: 1980 2013

Included observations: 34 Variable Coefficient Std. Error t-Statistic Prob. C -3E+07 1.25E+08 -0.238975 0.8127

MI 0.065441 0.006942 9.426316 0.000

ER -6367013 3857701 -1.650468 0.1089 R-squared 0.904487 Mean dependent var 3.67E+08

Adjusted R-squared 0.898325 S.D. dependent var 6.04E+08

S.E. of regression 1.93E+08 Akaike info criterion 41.07529

Sum squared resid 1.15E+18 Schwarz criterion 41.20997

Log likelihood -695.28 Hannan-Quinn criter. 41.12122

F-statistic 146.7824 Durbin-Watson stat 1.449059


2. MEASURE OF MULTI CO LINEARITY (VIF)


Date: 10/31/16 Time: 07:11

Sample: 1980 2013

Included observations: 34



C 1.57E+16 14.40111 NA

IMPORT 4.82E-05 9.581438 4.58209

EXRATE 1.49E+13 34.67286 4.58209



27

MEASURE OF AUTOCORRELATION L.M TEST (BEFORE REMOVAL):

3. O.L.S:



Date: 11/01/16 Time: 07:58

Sample: 1980 2013



C 1973.184 6.37067 309.7294 0

IMPORT 4.13E-09 3.34E-09 1.235642 0.2259

EXRATE 1.26499 0.425398 2.973661 0.0057

R-squared 0.68824

Mean

dependent var 1996.5

Adjusted R-squared 0.668126

S.D. dependent

var 9.958246

S.E. of regression 5.736793

Akaike info

criterion 6.415775

Sum squared resid 1020.235

Schwarz

criterion 6.550454

Log likelihood -106.068

Hannan-Quinn

criter. 6.461704

F-statistic 34.21773

Durbin-Watson

stat 0.217851

Prob(F-statistic) 0

4. MEASURE OF MULTI CO LINEARITY (VIF)


Date: 11/01/16 Time: 07:59

Sample: 1980 2013




C 40.58544 41.92864 NA

IMPORT 1.12E-17 5.639293 4.084345

EXRATE 0.180964 59.60694 4.084345



28

5. L.M TEST:

Breusch-Godfrey Serial Correlation

LM Test:


Prob.

F(1,30) 0

Obs*R-squared 21.64

Prob. Chi-

Square(1) 0

Test Equation:

Dependent Variable: RESID


Date: 11/01/16 Time: 07:59

Sample: 1980 2013


Presample missing value lagged

residuals set to zero.

Variable Coefficient Std. Error

t-

Statistic Prob.

C 7.254137 4.030841 1.799659 0.082

IMPORT 2.12E-09 2.07E-09 1.025987 0.3131

EXRATE -0.47301 0.268772 -1.7599 0.0886

RESID(-1) 0.840866 0.116024 7.247361 0

R-squared 0.63647

Mean

dependent var 1.60E-13


S.D.

dependent var 5.560234

S.E. of regression 3.516084

Akaike info

criterion 5.462704

Sum squared resid 370.8854

Schwarz

criterion 5.642276


Hannan-

Quinn criter. 5.523943


Durbin-

Watson stat 1.67693




29

6. MEASURE OF AUTOCORRELATION L.M TEST (AFTER REMOVAL):

Breusch-Godfrey Serial Correlation LM Test:


Obs*R-squared 0.273056

Prob. Chi-

Square(1) 0.6013

Test Equation:

Dependent Variable: RESID


Date: 10/31/16 Time: 07:12

Sample: 1980 2013


Presample missing value lagged residuals set to zero.

Variable Coefficient Std. Error

t-

Statistic Prob.

C 4363259 1.27E+08 0.03428 0.9729

IMPORT 0.001247 0.007471 0.16698 0.8685

EXRATE -316608 3958173

-

0.079988 0.9368

RESID(-1) 0.133538 0.270961 0.492831 0.6257

R-squared 0.008031

Mean

dependent var

-5.26E-

08

Adjusted R-squared -0.09117

S.D.

dependent var 1.87E+08

S.E. of regression 1.95E+08

Akaike info

criterion 41.12605

Sum squared resid 1.14E+18

Schwarz

criterion 41.30562


Hannan-Quinn

criter. 41.18729


Durbin-

Watson stat 1.472349




30

7. STABILITY ANALYSIS:

CUSUM AND CUSUM OF SQUARES TEST.

8. CHOW BREAK POINT TEST:

Chow Breakpoint Test: 2011

Null Hypothesis: No breaks at specified breakpoints

Varying regressors: All equation variables

Equation Sample: 1980 2013

F-statistic 28.74233 Prob. F(3,28) 0

Log likelihood ratio 47.80342

Prob. Chi-

Square(3) 0

Wald Statistic 86.22698

Prob. Chi-

Square(3) 0

-20

-15

-10

-5

0

5

10

15

20

84 86 88 90 92 94 96 98 00 02 04 06 08 10 12

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

84 86 88 90 92 94 96 98 00 02 04 06 08 10 12

CUSUM of Squares 5% Significance



31

9. UNIT ROOT TEST:

FDI

AT LEVEL:

Null Hypothesis: FDI has a unit root

Exogenous: Constant

Lag Length: 1 (Automatic - based on SIC, maxlag=1)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic 3.147339 1.0000

Test critical values: 1% level -3.653730

5% level -2.957110

10% level -2.617434

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(FDI)


Date: 10/31/16 Time: 07:19

Sample (adjusted): 1982 2013

Included observations: 32 after adjustments


FDI(-1) 0.317239 0.100796 3.147339 0.0038

D(FDI(-1)) -0.513497 0.244990 -2.095989 0.0449

C 8526787. 48783109 0.174790 0.8625

R-squared 0.261985 Mean dependent var 81175065







Null Hypothesis: FDI has a unit root

Exogenous: Constant, Linear Trend


t-Statistic Prob.*




32


5% level -3.557759

10% level -3.212361



Dependent Variable: D(FDI)


Date: 10/31/16 Time: 07:20




FDI(-1) 0.241096 0.171882 1.402690 0.1717

D(FDI(-1)) -0.483254 0.254001 -1.902565 0.0674

C -44717119 1.09E+08 -0.411757 0.6837

@TREND("1980") 4300617. 7812665. 0.550467 0.5864










33

AT 1ST DIFFERENCE:

Null Hypothesis: D(FDI) has a unit root

Exogenous: Constant


t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -4.669082 0.0007


5% level -2.957110

10% level -2.617434



Dependent Variable: D(FDI,2)


Date: 10/31/16 Time: 07:21




D(FDI(-1)) -1.132857 0.242630 -4.669082 0.0001

C 87717842 47594377 1.843030 0.0752










34

Null Hypothesis: D(FDI) has a unit root



t-Statistic Prob.*



5% level -3.557759

10% level -3.212361



Dependent Variable: D(FDI,2)


Date: 10/31/16 Time: 07:22




D(FDI(-1)) -1.319304 0.229242 -5.755077 0.0000

C -1.33E+08 90124084 -1.472370 0.1517

@TREND("1980") 13119753 4714351. 2.782940 0.0094










35

IMPORT:

AT LEVEL:

Null Hypothesis: IMPORT has a unit root

Exogenous: Constant


t-Statistic Prob.*



5% level -2.954021

10% level -2.615817



Dependent Variable: D(IMPORT)


Date: 10/31/16 Time: 07:23




IMPORT(-1) 0.090891 0.037057 2.452718 0.0200

C 1.50E+08 5.00E+08 0.300890 0.7655

R-squared 0.162520 Mean dependent var 1.05E+09







Null Hypothesis: IMPORT has a unit root



t-Statistic Prob.*



5% level -3.552973

10% level -3.209642




36


Dependent Variable: D(IMPORT)


Date: 10/31/16 Time: 07:23




IMPORT(-1) -0.008251 0.071368 -0.115608 0.9087

C -7.68E+08 7.50E+08 -1.023705 0.3142

@TREND("1980") 1.11E+08 69163818 1.610975 0.1177








AT 1ST DIFFERENCE:

Null Hypothesis: D(IMPORT) has a unit root

Exogenous: Constant


t-Statistic Prob.*



5% level -2.957110

10% level -2.617434



Dependent Variable: D(IMPORT,2)


Date: 10/31/16 Time: 07:24




D(IMPORT(-1)) -0.989914 0.184309 -5.370938 0.0000

C 1.06E+09 4.25E+08 2.503150 0.0180





37






Null Hypothesis: D(IMPORT) has a unit root



t-Statistic Prob.*



5% level -3.562882

10% level -3.215267



Dependent Variable: D(IMPORT,2)


Date: 10/31/16 Time: 07:25




D(IMPORT(-1)) -1.859047 0.303002 -6.135434 0.0000

D(IMPORT(-1),2) 0.467043 0.199622 2.339635 0.0269

C -1.87E+09 7.93E+08 -2.350759 0.0263

@TREND("1980") 2.16E+08 51266780 4.211493 0.0003






38





EXCHANGE RATE:

AT LEVEL:

Null Hypothesis: EXRATE has a unit root

Exogenous: Constant


t-Statistic Prob.*



5% level -2.960411

10% level -2.619160



Dependent Variable: D(ER)


Date: 10/31/16 Time: 07:26




ER (-1) -0.001238 0.020751 -0.059667 0.9529

D(ER (-1)) 0.265446 0.216136 1.228146 0.2300

D(ER (-2)) -0.771318 0.257002 -3.001217 0.0057

C 2.778871 1.079206 2.574923 0.0158

R-squared 0.250244 Mean dependent var 1.805979

Adjusted R-squared 0.166937 S.D. dependent var 2.031961

S.E. of regression 1.854617 Akaike info criterion 4.193147

Sum squared resid 92.86927 Schwarz criterion 4.378178






39

Null Hypothesis: EXRATE has a unit root



t-Statistic Prob.*



5% level -3.552973

10% level -3.209642



Dependent Variable: D(EXRATE)


Date: 10/31/16 Time: 07:26




EXRATE(-1) -0.364165 0.140450 -2.592845 0.0146

C 7.247168 2.153415 3.365431 0.0021

@TREND("1980") 0.671842 0.262311 2.561243 0.0157

R-squared 0.183182 Mean dependent var 1.898460







AT 1ST DIFFERENCE:

Null Hypothesis: D(EXRATE) has a unit root

Exogenous: Constant


t-Statistic Prob.*



5% level -2.960411

10% level -2.619160



40



Dependent Variable: D(EXRATE,2)


Date: 10/31/16 Time: 07:27




D(EXRATE(-1)) -1.508563 0.248192 -6.078209 0.0000

D(EXRATE(-1),2) 0.770703 0.252184 3.056110 0.0049

C 2.725159 0.584563 4.661875 0.0001

R-squared 0.579950 Mean dependent var -0.254536







Null Hypothesis: D(EXRATE) has a unit root



t-Statistic Prob.*



5% level -3.562882

10% level -3.215267



Dependent Variable: D(EXRATE,2)


Date: 10/31/16 Time: 07:27




D(EXRATE(-1)) -1.511073 0.253425 -5.962595 0.0000



41

D(EXRATE(-1),2) 0.767282 0.258115 2.972641 0.0061

C 2.642829 0.875165 3.019806 0.0055

@TREND("1980") 0.004893 0.038136 0.128302 0.8989

R-squared 0.580206 Mean dependent var -0.254536







10. HETEROSKEDASTICITY TEST:

Heteroskedasticity Test: White


Obs*R-squared 27.91543 Prob. Chi-Square(5) 0.0000

Scaled explained SS 104.2736 Prob. Chi-Square(5) 0.0000

Test Equation:

Dependent Variable: RESID^2


Date: 11/03/16 Time: 06:53

Sample: 1980 2013



C 8.85E+16 8.39E+16 1.054029 0.3009

IMPORT^2 0.002062 0.000360 5.725836 0.0000

IMPORT*EXRATE -1738612. 384315.9 -4.523915 0.0001

IMPORT 41080182 14087910 2.915988 0.0069

EXRATE^2 3.07E+14 1.01E+14 3.049421 0.0050

EXRATE -1.20E+16 5.72E+15 -2.099911 0.0449










42

11. COINTEGRATION TEST:

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.1

No. of CE(s) Eigenvalue Statistic

Critical

Value Prob.**

None * 0.877368 72.4078 27.06695 0

At most 1 0.205143 7.352203 13.42878 0.537

At most 2 0.007546 0.234815 2.705545 0.628

Trace test indicates 1 cointegrating eqn(s) at the 0.1

level

* denotes rejection of the hypothesis at the 0.1 level

**MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum

Eigenvalue)

Hypothesized

Max-

Eigen 0.1

No. of CE(s) Eigenvalue Statistic

Critical

Value Prob.**

None * 0.877368 65.0556 18.89282 0

At most 1 0.205143 7.117388 12.29652 0.4754

At most 2 0.007546 0.234815 2.705545 0.628

Max-eigenvalue test indicates 1 cointegrating eqn(s)

at the 0.1 level

* denotes rejection of the hypothesis at the 0.1 level


Unrestricted Cointegrating Coefficients (normalized

by b'*S11*b=I):

FDI IMPORT EXRATE

9.30E-09 -4.12E-10 0.06504

-1.53E-08 1.08E-09 -0.06779

-1.97E-10 3.71E-10 -0.153843



43

12. CAUSALITY ANALYSIS:

Pairwise Granger Causality Tests

Date: 10/31/16 Time: 07:31

Sample: 1980 2013

Lags: 1

Null Hypothesis: Obs

F-

Statistic Prob.

IMPORT does not Granger Cause FDI 33 9.57149 0.0043

FDI does not Granger Cause IMPORT 0.10728 0.7455

EXRATE does not Granger Cause FDI 33 1.84329 0.1847

FDI does not Granger Cause EXRATE 0.4007 0.5315

EXRATE does not Granger Cause IMPORT 33 2.6585 0.1135

IMPORT does not Granger Cause EXRATE 1.62268 0.2125

PANEL DATA OUTPUT RESULTS

13. O.L.S:


Method: Panel Least Squares

Date: 11/02/16 Time: 01:01

Sample: 1980 2013

Periods included: 34

Cross-sections included: 2

Total panel (balanced) observations:

68




44

C -3.58E+08 1.12E+09 -0.32059 0.7496

IMPORT 0.079876 0.004295 18.59866 0

EXRATE -729152 26146303 -0.02789 0.9778

R-squared 0.848021

Mean dependent

var 4.40E+09


S.D. dependent

var 9.88E+09

S.E. of regression 3.91E+09

Akaike info

criterion 47.05447



Hannan-Quinn

criter. 47.09327


Durbin-Watson

stat 0.752109

Prob(F-statistic) 0

14. UNIT ROOT TEST:

FDI:

Null Hypothesis: Unit root (individual unit root process)

Series: FDI

Date: 11/03/16 Time: 10:36

Sample: 1980 2013

Exogenous variables: Individual effects

User-specified lags: 1

Total (balanced) observations: 64


Method Statistic Prob.**

ADF - Fisher Chi-square 0.27446 0.9914

ADF - Choi Z-stat 3.94073 1.0000

** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

Intermediate ADF test results FDI



45

Cross

section Prob. Lag Max Lag Obs

1 1.0000 1 1 32

2 0.8718 1 1 32


Series: FDI

Date: 11/03/16 Time: 10:37

Sample: 1980 2013

Exogenous variables: Individual effects, individual linear trends






ADF - Choi Z-stat 3.05642 0.9989



Intermediate ADF test results FDI

Cross


1 1.0000 1 1 32

2 0.6668 1 1 32


Series: D(FDI)

Date: 11/03/16 Time: 10:38

Sample: 1980 2013







ADF - Choi Z-stat -2.49727 0.0063



Intermediate ADF test results D(FDI)



46

Cross


1 0.0789 1 1 31

2 0.0170 1 1 31


Series: D(FDI)

Date: 11/03/16 Time: 10:38

Sample: 1980 2013







ADF - Choi Z-stat -2.70296 0.0034



Intermediate ADF test results D(FDI)

Cross


1 0.0098 1 1 31

2 0.0683 1 1 31

IMPORT:


Series: IMPORT

Date: 11/03/16 Time: 10:40

Sample: 1980 2013







ADF - Choi Z-stat 5.23800 1.0000



47



Intermediate ADF test results IMPORT

Cross


1 1.0000 1 1 32

2 0.9992 1 1 32


Series: IMPORT

Date: 11/03/16 Time: 10:40

Sample: 1980 2013







ADF - Choi Z-stat 3.73992 0.9999



Intermediate ADF test results IMPORT

Cross


1 0.9981 1 1 32

2 0.9917 1 1 32


Series: D(IMPORT)

Date: 11/03/16 Time: 10:41

Sample: 1980 2013






48




ADF - Choi Z-stat -3.23709 0.0006



Intermediate ADF test results D(IMPORT)

Cross


1 0.0135 1 1 31

2 0.0090 1 1 31


Series: D(IMPORT)

Date: 11/03/16 Time: 10:41

Sample: 1980 2013







ADF - Choi Z-stat -4.99034 0.0000



Intermediate ADF test results D(IMPORT)

Cross


1 0.0001 1 1 31

2 0.0004 1 1 31



49

EXCHANGE RATE:


Series: EXRATE

Date: 11/03/16 Time: 10:43

Sample: 1980 2013







ADF - Choi Z-stat 1.94093 0.9739



Intermediate ADF test results EXRATE

Cross


1 0.9140 1 1 32

2 0.9160 1 1 32


Series: EXRATE

Date: 11/03/16 Time: 10:44

Sample: 1980 2013







ADF - Choi Z-stat -0.42905 0.3339





50

Intermediate ADF test results EXRATE

Cross


1 0.1275 1 1 32

2 0.7024 1 1 32


Series: D(EXRATE)

Date: 11/03/16 Time: 10:44

Sample: 1980 2013







ADF - Choi Z-stat -4.18589 0.0000



Intermediate ADF test results D(EXRATE)

Cross


1 0.0000 1 1 31

2 0.0377 1 1 31


Series: D(EXRATE)

Date: 11/03/16 Time: 10:45

Sample: 1980 2013







ADF - Choi Z-stat -3.27078 0.0005



51



Intermediate ADF test results D(EXRATE)

Cross


1 0.0002 1 1 31

2 0.1551 1 1 31

15. CO-INTERGRATION TEST:

Johansen Fisher Panel Cointegration Test

Series: FDI IMPORT EXRATE

Date: 11/02/16 Time: 01:14

Sample: 1980 2013


Trend assumption: Linear deterministic trend

Lags interval (in first differences): 1 1

Unrestricted Cointegration Rank Test (Trace and

Maximum Eigenvalue)

Hypothesized

Fisher

Stat.* Fisher Stat.*

No. of CE(s)

(from

trace

test) Prob.

(from max-

eigen test) Prob.

None 26.24 0 24.52 0.0001

At most 1 7.378 0.1172 8.396 0.0781

At most 2 0.804 0.9379 0.804 0.9379

* Probabilities are computed using asymptotic Chi-

square distribution.

Individual cross section results

Trace

Test

Max-Eign

Test

Cross Section Statistics Prob.** Statistics Prob.**

Hypothesis of no cointegration



52

1 55.6637 0 38.5104 0.0001

2 24.9055 0.1648 20.8147 0.0553

Hypothesis of at most 1 cointegration relationship

1 17.1533 0.0279 17.0546 0.0176

2 4.0909 0.8961 4.071 0.8518

Hypothesis of at most 2 cointegration relationship

1 0.0987 0.7534 0.0987 0.7534

2 0.0198 0.8879 0.0198 0.8879




53

APPENDI -B

Time series data

BANGLADESH

Years

FDI

(U.S.$)

Merchandise Import

(U.S. $)

Exchange Rate

(local currency per

U.S.$)

1980 8510000 2599000000 15.45405833

1981 5360000 2699000000 17.98669167

1982 6960000 2464000000 22.11788333

1983 403978.5575 2165000000 24.615425

1984 -553269.3983 2825000000 25.35393339

1985 -6660000 2542000000 27.99459167

1986 2436499.344 2546000000 30.4069

1987 3205086.762 2715000000 30.94983333

1988 1838242.499 3041000000 31.7332486

1989 247908.2739 3650000000 32.27

1990 3238781.189 3618000000 34.56880833

1991 1390444.322 3412000000 36.59618333

1992 3721853.382 3732000000 38.95075833

1993 14049886.52 3994000000 39.5672575

1994 11147788.33 4602000000 40.21173917

1995 1896372.127 6694000000 40.27831833

1996 13529831.54 7032000000 41.79416833

1997 139376153.1 7263000000 43.89211583

1998 190059373 7495000000 46.90565167

1999 179662970.3 8331000000 49.0854

2000 280384629.7 8883000000 52.14166667

2001 78527040.08 9018000000 55.80666667

2002 52304931.04 8592000000 57.888

2003 268285231.8 10434000000 58.15004

2004 448905400.7 12036000000 59.51265833

2005 760504265.8 13889000000 64.327475

2006 456523167.7 16034000000 68.93323333

2007 651029738.1 18596000000 68.874875

2008 1328422987 23860000000 68.598275

2009 901286583.1 21833000000 69.03906667

2010 1232258247 27821200000 69.64929167

2011 1264725163 36213900000 74.1524

2012 1584403460 34173100000 81.86265833

2013 2602962095 37085100000 78.103235



54

Panel data

BANGLADESH INDIA

Year FDI

(U.S.$)

Merchandise

Import

(U.S. $)

Exchange Rate

(local currency

per U.S.$)

FDI

(U.S.$)

Merchandise

Import

(U.S.$)

Exchange

Rate

(local

currency per

U.S.$)

1980 8510000 2599000000 15.45405833 79160000 1.49E+10 7.862945

1981 5360000 2699000000 17.98669167 91920000 1.54E+10 8.658523

1982 6960000 2464000000 22.11788333 72080000 1.48E+10 9.455132

1983 403978.5575 2165000000 24.615425 5640000 1.41E+10 10.0989

1984 -553269.3983 2825000000 25.35393339 19240000 1.53E+10 11.36258

1985 -6660000 2542000000 27.99459167 1.06E+08 1.59E+10 12.36875

1986 2436499.344 2546000000 30.4069 1.18E+08 1.54E+10 12.61083

1987 3205086.762 2715000000 30.94983333 2.12E+08 1.67E+10 12.9615

1988 1838242.499 3041000000 31.7332486 91250000 1.91E+10 13.91708

1989 247908.2739 3650000000 32.27 2.52E+08 2.05E+10 16.2255

1990 3238781.189 3618000000 34.56880833 2.37E+08 2.36E+10 17.5035

1991 1390444.322 3412000000 36.59618333 73537638 2.04E+10 22.74243

1992 3721853.382 3732000000 38.95075833 2.77E+08 2.36E+10 25.91808

1993 14049886.52 3994000000 39.5672575 5.5E+08 2.28E+10 30.49329

1994 11147788.33 4602000000 40.21173917 9.73E+08 2.68E+10 31.37374

1995 1896372.127 6694000000 40.27831833 2.14E+09 3.47E+10 32.42708

1996 13529831.54 7032000000 41.79416833 2.43E+09 3.79E+10 35.43317

1997 139376153.1 7263000000 43.89211583 3.58E+09 4.14E+10 36.31329

1998 190059373 7495000000 46.90565167 2.63E+09 4.3E+10 41.25937

1999 179662970.3 8331000000 49.0854 2.17E+09 4.7E+10 43.05543

2000 280384629.7 8883000000 52.14166667 3.58E+09 5.15E+10 44.94161

2001 78527040.08 9018000000 55.80666667 5.13E+09 5.04E+10 47.18641

2002 52304931.04 8592000000 57.888 5.21E+09 5.65E+10 48.61032

2003 268285231.8 10434000000 58.15004 3.68E+09 7.26E+10 46.58328

2004 448905400.7 12036000000 59.51265833 5.43E+09 9.98E+10 45.31647

2005 760504265.8 13889000000 64.327475 7.27E+09 1.43E+11 44.09998

2006 456523167.7 16034000000 68.93323333 2E+10 1.78E+11 45.30701

2007 651029738.1 18596000000 68.874875 2.52E+10 2.29E+11 41.34853

2008 1328422987 23860000000 68.598275 4.34E+10 3.21E+11 43.50518

2009 901286583.1 21833000000 69.03906667 3.56E+10 2.57E+11 48.40527

2010 1232258247 27821200000 69.64929167 2.74E+10 3.5E+11 45.72581

2011 1264725163 36213900000 74.1524 3.65E+10 4.64E+11 46.67047

2012 1584403460 34173100000 81.86265833 2.4E+10 4.9E+11 53.43723

2013 2602962095 37085100000 78.103235 2.82E+10 4.65E+11 58.59785

Documents

Secondary report SAAD Group