Appraising Inflation Targeting. Panel Evidence From Developed Economies

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MSc Economics thesis 2010-2011

Department of Economics, University of

Bristol

MANHAL M ALI


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APPRAISING INFLATION TARGETING: PANEL EVIDENCE FROM

DEVELOPED ECONOMIES

NAME OF AUTHOR: MANHAL M ALI

A THESIS SUBMITTED TO THE UNIVERSITY OF BRISTOL IN ACCORDANCE WITH THE

REQUIREMENTS OF THE DEGREE OF MSc ECONOMICS IN THE FACUALTY OF SOCIAL

SCIENCES AND LAW

DEPARTMENT OF ECONOMICS, UNIVERSITY OF BRISTOL

SEPTEMBER, 2011


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Abstract

By using dynamic panel GMM techniques this paper finds that in general that inflation

targeting (IT) regime has not led to improvement or was positively effective in terms of

macroeconomic performance in terms of inflation, output growth, inflation volatility

and output volatility. Hence reinforcing, in summary IT was mainly ineffective. There is

some evidence IT had positive impact on inflation, inflation volatility and output growth

but it is not robust and not general. At best there is no indication that IT had adverse

effects on economic stabilization or volatility. There is also no conclusive evidence that

IT has worsened or led to more favourable tradeoffs between inflation and economic

activity. The general results of this paper also align with results of some previous

researches in this field.


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WORD COUNT

Number of pages: 60

Number of words: 14,992 (including title, abstract and pages 1 to 44 only).


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ACKNOWLEDGMENTS

I have benefited from the discussions that I had with my thesis supervisors Dr. Helene

Turon and Professor Fabien Postel-Vinay and suggestions that I have received from

them. My sincere recognition goes to them. I would like to specially thank Dr. Helene

Turon and my academic supervisor Professor Simon Burgess for their kind support to

help me carry out this thesis. I would also like to thank Professor Jon Temple for kindly

making one of his papers available to me in order to read on applied work using panel

GMM. I gratefully acknowledge the help I have received from thesis help desk regarding

the use of Stata software from Jake Bradley, a senior PhD student at the Department of

Economics, University of Bristol.

Lastly, I would like to dedicate this work to my parents who were extremely supportive

all the way from the beginning. It would have not been possible without them.


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AUTHORS DECLARATION

I declare that the work in this dissertation/thesis was carried out in accordance with

the regulations of the University of Bristol. The work is original except where indicated

by special reference in the text and no part of the thesis has been submitted by other

degree.

Any views expressed in the thesis are those of the author and in no way represent those

of the University of Bristol.

The thesis has not been presented to any other University for examination either in the

United Kingdom or overseas.

SIGNED: DATE:


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TABLE OF CONTENTS

1. INTRODUCTION ................................................................................................................................................ 1

2. INFLATION TARGETING IN THEORY ...................................................................................................... 2

3. PREVIOUS STUDIES ........................................................................................................................................ 6

4. DATA ....................................................................................................................................................................... 9

5. METHODOLOGY .............................................................................................................................................. 17

6. RESULTS ............................................................................................................................................................ 21

6.1. PRELIMANARY RESULTS ............................................................................................................... 21

6.2. 1985-2002 ........................................................................................................................................... 28

6.3. ROBUSTNESS ANALYSIS .................................................................................................................... 31

6.4. INFLATION-OUTPUT TRADEOFF .................................................................................................... 37

7. LIMITATIONS AND EXTENSIONS ......................................................................................................... 41

8. CONCLUSION ................................................................................................................................................... 43

REFERENCES ....................................................................................................................................................... 45

LIST OF FIGURES

4.1. AVERAGE INFLATION ....................................................................................................................... 14

4.2. INFLATION VOLATILITY................................................................................................................... 15

4.3. AVERAGE OUTPUT GROWTH .......................................................................................................... 16

4.4. OUTPUT VOLATILITY........................................................................................................................ 16

LIST OF TABLES

4.1. COUNTRIES INCLUDED IN THE SAMPLE.......................................................................................... 9

4.2. INFLATION STATISTICS FOR INFLATION TARGETING COUNTRIES......................................... 10

4.3. INFLATION STATISTICS FOR NON-INFLATION TARGETING COUNTRIES............................... 11

4.4. OUTPUT STATISTICS FOR INFLATION TARGETING COUNTRIES.............................................. 12

4.5. OUTPUT STATISTICS FOR NON-INFLATION TARGETING COUNTRIES .................................... 13

6.1. ESTIMATES OF INFLATION TARGETING EFFECTS ON INFLATION AND GROWTH (1980-2009)............................................................................................................................................................ 22


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6.2. ESTIMATES OF INFLATION TARGETING EFFECTS ON MACROECONOMIC VOLATILITY

(1980-2009)................................................................................................................................................ 24

6.3. ESTIMATES OF INFLATION TARGETING EFFECTS ON INFLATION AND GROWTH (1985-

2002)............................................................................................................................................................ 29

6.4. ESTIMATES OF THE INFLATION TARGETING EFFECTS ON MACROECONOMIC VOLATILITY(1985-2002)................................................................................................................................................ 30

6.5. ESTIMATES OF INFLATION TARGETING EFFECTS ON INFLATION AND GROWTH,

ROBUSTNESS CHECKS............................................................................................................................... 33

6.6. ESTIMATES OF INFLATION TARGETING EFFECTS ON MACROECONOMIC VOLATILITY,


6.7. ESTIMATES OF INFLATION TARGETING EFFECTS ON COEFFICIENT OF VARIATIONS OF

INFLATION AND OUTPUT GROWTH, ROBUSTNESS CHECKS ............................................................. 36

6.8. ESTIMATES OF INFLATION TARGETING EFFECTS ON INFLATION-OUTPUT TRADEOFF

(1980-2009)................................................................................................................................................ 38

6.9. ESTIMATES OF INFLATION TARGETING EFFECTS ON INFLATION-OUTPUT TRADEOFF

(1985-2002)................................................................................................................................................ 39

6.10. ESTIMATES OF INFLATION TARGETING EFFECTS ON INFLATION-OUTPUT TRADEOFF,



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1. INTRODUCTION

One of the central objectives of the central banks worldwide is to promote

macroeconomic stability by stabilizing and lowering inflation. Several economies,

industrial and emerging markets implemented various monetary policy regimes to

achieve this objective. A regime that has received significant attention recently is

Inflation Targeting. It was first pioneered and adopted by New Zealand in 1990. In

recent years there has been increasing number of countries that adopted inflation

targeting to help to stabilize inflation and promote economic stability. But has inflation

targeting been successful as a monetary policy regime to achieve the aforementioned

objective and in terms of general macroeconomic performance? Certainly from the data

both developed and emerging economies saw reductions in inflation rates since

adopting this monetary regime. But countries that did not adopt this regime also

experienced fall in inflation rates. So did inflation targeting lead to fall in inflation rates

from point of view of formal statistical analysis? Did inflation targeting produced

smaller costs in terms of output, was IT favourable to the real economy and managed to

reduce volatility?

Since its introduction there has been a surge in research on inflation targeting

concerning its effectiveness. So far, the empirical results on this topic are mixed and

inconclusive. Results vary according to the methods used and samples selected.

Nevertheless this area still remains active an area of research and is debated in

academia and central banks worldwide. The objective of this paper is to enter this

debate and to answer the questions posed at the beginning i.e. whether inflation

targeting countries benefited in terms of key macroeconomic performance, using

dynamic panel techniques for the case of developed economies.

This paper uses the dynamic panel GMM techniques i.e. Difference GMM (D-GMM)

due to Holtz-Eakin et al. (1988) and Arellano and Bond (1991) and System GMM (S-

GMM) due to Arellano and Bover (1995) and Blundell and Bond (1998) to assess

whether inflation targeting was effective or improved the macroeconomic performance

of developed economies. In general there is no evidence that inflation targeting

mattered or in other words inflation targeting was not found to be positively effective.


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The paper is divided into eight sections. After this introduction, section two looks

briefly at the theory. Section three present reviews previous literature along with the

contribution of this paper. Section four is concerned with the data and descriptive

analysis. Section five presents the methodology. Section six presents the econometricresults. Section seven considers extensions and limitations. Section eight concludes.

2. INFLATION TARGETING IN THEORY

Inflation targeting (henceforth IT) is a monetary policy framework where the sole

objective of the central bank adopting it is to promote price stability by committing

itself to achieve an explicit target or range for inflation rate by using interest rates or

other monetary options. The objective function facing a central bank operating under IT

regime is given in equation (2.1):

(2.1)

Equation (2.1) is the loss function that central bank minimizes were is theinflation rate and is the output gap, at time t. The parameter is the weight that thesociety places on output stabilization relative to inflation stabilization and is thetarget inflation rate. As long as , specifying IT in terms of the social loss functionassumes that the central bank is concerned with both output and inflation stabilization

if then IT regime is said to be flexible.

Since policy has a lagged effect, an assumption is made that central bank must set,nominal interest rate at time t, prior to observing any information at time t. This implies

that central bank cannot act to shocks at time tcontemporaneously. Information aboutshocks at time twill affect the choice of , and . The central banks objectiveis to then minimize (2.2) by choosing :

(2.2)where the subscript on the expectations operator is now t-1 to reflect that information

available to central bank when it sets its policy, where the constraints are given by IS

and New Keynesian Phillips curve given in equations (2.3) and (2.4) respectively:


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(2.3)

(2.4)

where the cost shock et-1 follows an AR (1) process. The first order condition under

discretion1for central banks choice of is given by:

(2.5)

Rearranging this first-order condition yields2:

(2.6)

Hence if the central bank forecasts that inflation in period twill exceed the target

rate then it should adjust monetary policy to ensure that the forecast of the output gap

is negative from (2.6).

IT consists of the following important elements: (1) Public announcement of a

medium term target for inflation which is usually quite low (usually specified as a few

range of percentage points). (2) Institutional commitment to price stability as the chief

long run monetary policy goal. (3) Increased transparency through communication withpublic and markets about the monetary policy objectives. (4) Increased accountability

of the central bank for attaining its inflation objectives. Batini and Laxton (2007)

mentions the pre conditions that are needed to be met before IT can be adopted.

One main advantage of IT due to its credibility and transparency elements is that it

solves the inflation bias problem due to dynamic inconsistency theory of inflation

(Kydland and Prescott, 1977) thus leading to lower inflation rates. Again due to the

policy being transparent and credible it is understood by the public and therefore it can

anchor the expected inflations and can lock in expectations of low inflation which

helps to contain the possible inflationary impact of macroeconomic shocks. Also in the

spirit of Barro and Gordons (1983) reputation model, central banks can establish a

1Under discretion the policy maker or the central bank chooses inflation taking expectations of inflation as

given and solves the optimisation problem every period (Walsh, 2010).2

See Walsh (2010) for details on the micro-foundations of the IS and New Keynesian Phillips curve and first

order conditions. (2.5) can be derived by differentiation of the discretionary monetary policy problem with

respect to and and then combining them into one equation. See page 361 of Walsh (2010).


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reputation of being tough against inflation in the context of infinitely repeated games

where subgame perfect Nash equilibrium exists with inflation lower than discretionary

inflation.

By anchoring expected inflations towards the target range, IT can reduce the impact

of shocks to the economy thereby leading to greater economic stability (Mishkin, 1999).

Another way of seeing this is that in the loss function (2.2) above, given , centralbanks implementing as the target also brings about reduced output variability i.e.central bank also cares about output stabilization. Since inflation target is a medium

term objective where the central banks target inflation over a certain horizon and given

that inflation cannot be controlled instantaneously, short term deviations from the

target are acceptable and do not necessarily translate into losses in credibility. This

increased flexibility also leads to lower output variability. By maintaining low inflation

and inflation volatility, IT also helps to promote output growth (Mishkin, 1999). Also

two channels in which IT can lead to output growth is through productivity enhancing

and finance growth nexus (Mollick et al. 2011). That is transparency, credibility and

certainty associated with IT can lead to better financial sector developments, more

domestic and foreign investments which in turn help to promote growth.

However IT has its disadvantages and hence beneficial claims made by its advocates

are rebutted. Critics argue that due to increased weight on inflation it offers little

discretion and this rigidity unnecessarily restrains growth and increases output

volatility. Also since targets can be changed and since it offers too little discretion, IT

cannot anchor expected inflations. For inflation to be successful the central bank must

demonstrate its commitment to low and stable inflation through tangible actions. In the

initial periods after adoption, to establish this reputational equilibrium of being tough

against inflation will require aggressive measures and extra conservatism which will

harm output growth. Generally IT constrains discretion inappropriately; it is too

constrictive (see loss function (2.2)) in terms of ex ante commitment to a particular

inflation number and a particular horizon over to which to return inflation to target

(Batini and Laxton, 2007). Growth can be restrained if it obliges the central bank to hit

the target very restrictively. Furthermore there are measurements and implementation

issues, for instance which measure of inflation should the central bank aim to target

(Bernanke et al., 1999; Mishkin and Posen, 1997). IT sceptics worry that pursuing rigid


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and low inflation target rates for example 1% can lead economies to hit the zero lower

bound-real interest rates become negative as nominal rates cannot be zero. In such

situations it can be challenging and prolonging to stimulate the economy especially at

the same time economy is concerned with also high inflation. Hence rigid and very lowtarget inflation targeting may lead to liquidity trap- a situation where nominal interest

rate is zero and monetary policy is powerless (Romer, 2006). Critics argue that IT

matters less for inflation and its stability and thus it is merely a conservative window

dressing. They argue it is the central banks greater emphasis and aversion towards

inflation that is important and not IT per se.

The credibility effects can lead to better tradeoffs because policy changes can affect

expected as well as actual inflation a central bank which agents believe will be

inflation hawk in the future will not have to contract output by as much today to achieve

a given disinflation Phillips curve becomes steeper. Furthermore, a credible

disinflation policy widely believed by agents or general public will cause inflation

expectation to decline rapidly and thereby shift down the Phillips curve without a large

output loss and hence resulting in smaller output losses and society having to pay lower

sacrifice ratio. This is sometimes referred as credibility bonus. It is commonly argued

that enhanced communication and accountability of the central bank under IT should

make announced inflation objectives more credible and hence disinflations less costly.

However there are problems with this result. If higher credibility leads to greater

nominal wage price rigidity for instance by perpetuating labour contracts, then this

can offset direct effects of improved credibility. For instance when a credible monetary

regime produces low inflation environment, firms does not change their prices

frequently and are less afraid to catch up if costs rise. And as the central banks become

more inflation averse, labour unions may choose less wage indexation and perpetuate

their wage contracts implying greater wage-price rigidity and hence flatter Phillips

curve (Clifton et al., 2001). Hutchison and Walsh (1998) mention that lower average

inflation by establishing credibility can increase nominal rigidity and worsen the

tradeoff Phillips curve becomes flatter the net effect is ambiguous.

In the following sections, the paper applies panel data analysis to test the above

theoretical claims made by proponents and critics of IT.


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3. PREVIOUS STUDIES

Since the introduction and adoption of IT in the 1990s, there has been growing active

research on whether implementation of this new monetary regime has been beneficial

in terms of macroeconomic performance. So far the empirical studies are mixed and

inconclusive, thus lacking consensus among researchers regarding the effectiveness of

IT.

One key seminal contribution to this literature is due to Ball and Sheridan (2005)

who analyse economic performance of IT using OECD economies. Using cross sectional

difference-in-difference estimation, Ball and Sheridan (2005) find no evidence that

adoption of this regime leads to improvement in economic performance i.e. inflation,

growth and volatilities. Using similar procedure Christensen and Hansen (2007) for

OECD economies from 1970 to 2005 found countries that have switched either to

exchange rate regime or IT experienced improvements in inflation, output and

volatilities but former regime lead to better performance. Mollick et al. (2011) for the

period 1986 to 2004 using static panel data techniques finds that adoption of IT leads to

higher output per capita for both developing and industrial economies. However under

dynamic specifications the evidence is rather weak. Wu (2004) and Willard (2006)

assessed the performance of IT for industrial economies using Difference GMM (D-

GMM). Wu (2004) using quarterly data from 1985 to 2002 finds that IT has been

effective in reducing inflation rates in the industrial countries. However revising the

findings, Willard (2006) finds no such evidence. Mishkin and Schmidt-Hebbel (2007)

using panel and instrumental variable (IV) estimation procedure with time and country

fixed effects, suggest that IT has been favourable to macroeconomic performance for

both industrial and emerging economies. However despite these results they find no

evidence that IT countries produced better monetary policy outcomes relative to non-IT

countries. Biondi and Toneto (2008) for 51 countries from 1995 to 2004 uses D-GMM

and S-GMM including time effects and Feasible Generalized Least squares with time and

random country effects. Biondi and Toneto (2008) find no benefits to output growth

due to IT adoption among developing economies however it was successful in reducing

inflation rates. The findings are opposite for developed economies but smaller in

magnitude. According to Mishkin (2004) institutional differences make inflation

targeting much more difficult operate in emerging economies than in developed


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economies. However others argue practicing IT leads to better macroeconomic

outcomes in developing economies (Bernanke et al., 1999; Svensson, 1997). Goncalves

and Salles (2008) using the methodology of difference-in-difference for the case of

emerging economies from 1980 to 2005 finds that IT is effective in terms of averageinflation, growth and output volatility. However Brito and Bystedt (2010) from 1980 to

2006 using S-GMM and other dynamic panel techniques using both common time and

country fixed effects for the case of emerging economies finds no empirical evidence

that IT matters in terms of behaviour of inflation, output growth, volatilities and found

that IT did not lead to favourable output inflation tradeoffs.

Using different methodologies, Lin and Ye (2007) using propensity score matching

methods for seven industrial countries from 1985 to 1999 find no evidence that IT had

impacts on inflation and on its volatility. Walsh (2009) using a similar methodology

finds no evidence that IT was effective in reducing inflation and economic volatility

among developed economies however results are more favourable for developing

economies. Nevertheless Vega and Winkelried (2005) also using propensity score

matching methods for a sample of developed and emerging economies find robust

evidence that IT has helped reduce inflation and its volatility. Peturrson (2004) using

Seemingly Unrelated Regression finds that inflation has fallen after IT adoption

however it is statistically insignificant when lagged inflation is used as an additional

control but remain significant for some countries. Affect of IT on output growth is

significant or borderline significant but find that output and inflation volatility had

fallen after the adoption of this regime. Goncalves and Carvalho (2007) for OECD

economies using Heckmans procedure find that IT countries suffered smaller output

loses during disinflation. However revising their findings, Brito (2009) again for OECD

economies using panel GMM techniques finds no such evidence of a favourable tradeoff

between inflation and output.

As seen from above, results vary according to methodologies and data sets. However

panel data has the advantage that it leads to more observations than cross sectional

data. Also by exploiting the time and country dimensions, it can isolate improvements

due to IT monetary regime from other sources that might be overlapping in a cross

sectional framework. By introducing country fixed effects panel data can address the

issue of omitted variable bias inherent in above studies for example Ball and Sheridan


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(2005) and lead to improvement on inference on the causal impact of IT on

macroeconomic indicators of interest. According to Biondi and Toneto (2008) panel

data is more informative, provides more efficient estimates of parameters, allowing the

study of dynamics and control for unobserved heterogeneity of individual countries.Most of the findings above fail to take into account the short run relationship between

inflation variability and real economic activity as implied by the Accelerationist Phillips

curve because as Mankiw (2001) mentions that inflation-output tradeoff is inexorable.

Therefore not acknowledging this tradeoff casts doubt on some of the findings

regarding IT as an effective monetary policy strategy for economic performance. As

Brito and Bystedt (2010) mentions, inflation reduction in isolation simply implies that

IT central banks are more risk averse towards inflation than non-IT counterparts. As far

as the difference-in-difference estimation procedure is concerned, Bertrand et al.

(2004) mention that it may erroneously produce causal relationship between IT

adoption and macroeconomic indicator and it also ignores vital time series information

in the data. This approach does not take into account the endogenous choice of IT

adopted by countries with different observable and unobservable characteristics (Uhlig,

2004). Although the propensity score methods deal with self selection problems, its

cross sectional nature does not control for time effects, unobserved country

heterogeneity and persistence. Given that it ignores past information the IV within

group estimation procedure of Mishkin and Schmidt-Hebbel (2007) is not efficient. The

random effect analysis used by Biondi and Toneto (2008) is not suitable as individual

specific effects can be correlated with the explanatory variables and do not consider the

impact IT regime on volatilities. The S-GMM is opted over D-GMM used by Wu (2004)

and Willard (2006) because of efficiency gains reason and S-GMM estimator is better

instrumented to capture the effects of high persistent variables (Arellano and Bover,

1995; Blundell and Bond 1998). Brito and Bystedt (2010) uses two-step S-GMM

estimator but only for the case of emerging economies. As mentioned above

institutional differences and weaknesses, preconditions (for instance technical

capability of the central bank, absence of fiscal dominance and sound financial markets)

and relatively late adoption imply that IT will have less favourable and desired

macroeconomic impacts in emerging economies.


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The aim of this paper is to re-assess the impact of IT on macroeconomic

performance by taking into account some of the shortcomings and discrepancy in the

above findings. Hence the aim and the contribution of this paper to the existing studies

is to study the impact of IT on inflation and output growth, on their volatilities and onthe inflation-output tradeoffs for developed economies from 1980 to 2009 using S-GMM

due to Arellano and Bover (1995) and Blundell and Bond (1998), also conducting

extensive robustness analysis.

4. DATA

The data consists of an unbalanced panel of 39 developed economies3 from the period

1980 to 2009. Table 4.1 lists the economies included in the data.

Table 4.1: Countries included in the sample4

Inflation Targeting Year of adoption Inflation target rate Non Inflation

countries Targeting countries

Australia 1993 2-3% Austria Netherlands

Canada 1991 1-3% Belgium Portugal

Chile 1991 2-4% Denmark Singapore

Czech Republic 1997 3%(1%) Cyprus Slovakia

Hungary 2001 3% (1%) Estonia Slovenia

Iceland 2001 2.5%(1.5%) Finland Spain

Israel 1992 1-3% France Switzerland

South Korea 1998 3%(1%) Germany Taiwan

Mexico 1999 3%(1%) Greece USA

New Zealand 1990 1-3% Hong Kong SAR

Norway 2001 2.5%(1%) Ireland

Poland 1998 2.5%(1%) Italy

Sweden 1993 2%(1%) JapanTurkey 2006 6.5%(2%) Luxembourg

UK 1992 2%(1%) Malta

The data consists of 15 economies that are IT and 24 that are non-IT. The data for the

countries inflation and real GDP growth rates were taken from IMFs World Economic

3According to IMF 34 economies in the sample are classified as advanced economies. Chile, Hungary, Israel,

Mexico and Turkey being members of OECD are regarded as developed countries.4

Adoption dates taken from Roger (2010) and Goncalves and Salles (2008).


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outlook database and World Banks World Development Indicators. The GDP series for

Estonia, Slovakia and Slovenia starts from 1981, 1985 and 1991 respectively whereas

for inflation it starts from 1992 for Slovakia and Slovenia and 1990 for Estonia.

In table 4.2 all countries that have adopted IT according to adoption dates in table

4.1 ex-post experienced lower average inflation rates and inflation volatility as

measured by standard deviation (SD) of inflation rates. Inflation rate is measured as

percentage change in Consumer Price Index (CPI) where base year is country specific.

Table 4.2: Inflation statistics for Inflation Targeting (IT) Countries

Entire Sample Pre-IT Post-ITMean SD Mean SD Mean SD

Australia 4.69 3.21 7.36 3.02 2.71 1.31

Canada 3.61 2.96 6.35 3.12 1.82 0.74

Chile 12.21 9.56 21.79 7.32 5.82 4.05

Czech Republic 6.35 10.59 8.3 13.67 3.39 2.83

Hungary 12.42 8.59 15.29 8.75 5.29 1.55

Iceland 16.56 20.44 20.94 23.09 6.3 3.89

Israel 43.01 83.55 99.49 112.12 4.96 4.28

Mexico 31.56 35 46.21 37.47 5.22 1.7

New Zealand 5.55 5.38 11.87 4.81 2.2 1.01Norway 4.29 3.41 5.28 3.6 1.84 1.07

Poland 49.4 112.28 79.33 138.3 3.85 2.83

South Korea 5.75 5.72 7.39 6.8 2.9 1.04

Sweden 4.07 3.62 7.12 3.61 1.67 0.73

Turkey 50.51 29.64 56.93 26.4 8.48 2.11

UK 4.05 3.52 7.03 3.91 1.93 0.69

IT15* 16.94 22.5 24.55 26.4 3.56 1.98

Note:

*The average of statistics above

Excludes Turkey since it adopted in 2006 which is late compared to other IT countries

The average inflation rates for IT countries fell from 24.55% in the pre targeting period

to 3.56% in end of post targeting period, an average by 20.99%. The volatility of

inflation measured by the standard deviation of inflation rates also dipped from 26.4%

to 1.98%. According to table 4.2, IT has been beneficial to the inflation outcomes of all IT

countries and an important reason why central banks seem happy with their choice.


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Table 4.3 reports inflation statistics for non-IT countries also for the periods prior and

after 1990.

Table 4.3: Inflation Statistics for Non-Inflation Targeting countries

Entire Sample Pre-1990 Post-1990

Mean SD Mean SD Mean SD

Austria 2.6 1.61 3.8 2.06 1.96 0.9

Belgium 3.02 2.26 4.9 2.91 2 0.95

Cyprus 4.06 2.74 5.77 3.74 3.14 1.59

Denmark 3.51 2.85 6.33 3.46 2.04 0.59

Estonia 77.57 238.32 80.74 244.4

Finland 3.74 3.22 7.28 3.04 1.82 1.06

France 3.71 3.64 7.34 4.38 1.81 0.78

Germany 2.32 1.64 2.9 2.2 1.99 1.26

Greece 11.45 8.28 19.5 4.07 6.41 5.18

Hong Kong SAR 4.71 4.83 7.43 2.86 2.99 4.96

Ireland 4.87 5.14 9.26 6.96 2.63 1.47

Italy 5.95 5.43 11.43 6.3 3.06 1.44

Japan 1.16 1.92 2.53 2.29 0.34 1.16

Luxembourg 3.46 3.19 5.78 4.68 2.22 0.89

Malta 2.64 2.29 2.27 3.82 2.8 1.01

Netherlands 2.48 1.76 2.84 2.8 2.29 0.99

Portugal 8.35 7.9 16.67 7.86 3.7 2.7

Singapore 2.07 2.3 2.77 3.27 1.62 1.6

Slovakia 7.66 5.21 7.66 5.21

Slovenia 19.72 47.4 19.72 47.4

Spain 5.85 4.09 10.25 3.94 3.49 1.6

Switzerland 2.19 1.88 3.27 1.78 1.45 1.51

Taiwan 2.83 4.35 4.64 7.03 1.81 1.66

USA 3.71 2.58 5.55 3.62 2.65 0.98

Non-IT24* 7.9 15.2 6.79 3.96 2.49 1.63

Note:


Excludes Estonia, Slovakia and Slovenia.

As these countries did not adopt IT, there is natural breaking point into pre and post

periods and hence the choice of the year 1990 is arbitrary, but rather it serves to

illustrate how the era of IT has largely been an era of low and stable inflation for both IT

and non-IT countries as table 4.3 illustrates. From table 4.3 all non-IT countries also

experienced low and stable inflation except for Malta which only experienced fall in


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inflation volatility. Hence it is evident that these countries have less incentive to pursue

IT as inflation rates were fairly low and stable. A simple difference-in-difference

comparison suggests some impact of IT as inflation fell from 28.41% to 8.96% between

the pre-1990 period and end of post-1990 period, a fall of 19.45% for IT countriescompared to 7.9% to 2.49%, a decrease of 5.41%.

As it has been noted earlier, greater emphasis of inflation stabilization and explicit

targeting will conflict with other macroeconomic goals i.e. real economy objectives and

lead to greater output volatility as can be seen from the loss function (2.2). Intermediate

monetary economics especially in short run generally suggest a tradeoff between

inflation and output stabilization. Hence in accordance with this, IT which puts more

weight on inflation stabilization should lead to greater output volatility. Tables 4.4 and

4.5 illustrate the average growth rates and output volatilities measured by the standard

deviation of growth rates for IT and non-IT countries from 1980 to 2009.

Table 4.4: Output growth statistics for Inflation Targeting (IT) Countries

Entire Sample Pre-IT Post-IT


Australia 3.26 1.71 2.82 2.33 3.57 0.99

Canada 2.53 2.22 2.78 2.44 2.63 1.88

Chile 4.56 4.77 3.58 6.67 4.97 3.33

Czech Republic 1.81 3.68 1.28 4.02 2.85 3.13

Hungary 1.27 3.66 0.958 3.75 1.79 3.72

Iceland 2.92 3.51 2.9 3.1 2.84 4.8

Israel 4.21 2.51 3.85 1.88 4.3 2.89

Mexico 2.55 3.78 2.91 4.04 1.75 3.51

New Zealand 2.33 2.23 1.94 1.99 2.65 2.37

Norway 2.75 1.79 3.19 1.75 1.68 1.62

Poland 2.25 4.62 1.03 5.5 4 1.9

South Korea 6.57 4.18 8.22 3.34 4.98 2.99

Sweden 2.08 2.39 1.87 1.87 2.55 2.54

Turkey 4.01 4.47 4.33 4.39 0.21 4.69

UK 2.14 2.2 2.02 2.44 2.34 2.09

IT15* 3.02 3.18 2.81 3.23 3.06 2.7

Note:


Excludes Turkey


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Table 4.5: Output growth Statistics for Non-Inflation Targeting countries

Entire Sample Pre-1990 Post-1990


Austria 2.04 1.59 1.82 1.16 2.05 1.77

Belgium 2.02 1.63 2.16 1.6 1.89 1.72

Cyprus 4.72 2.74 6.13 1.99 3.84 2.79

Denmark 1.72 2.17 1.9 2.22 1.63 2.25

Estonia 1.9 7.55 2.74 1.58 1.98 9.09

Finland 2.43 3.3 3.55 1.3 1.94 3.94

France 1.88 1.43 2.35 1.19 1.6 1.52

Germany 1.7 1.97 1.87 1.48 1.37 2.03

Greece 2 2.32 0.78 2.3 2.75 2.09

Hong Kong SAR 5.07 4.13 7.44 4.25 3.88 3.7

Ireland 4.29 4.04 2.4 1.76 5.1 4.62Italy 1.33 1.86 2.06 1.74 0.91 1.89

Japan 2.16 2.67 4.4 1.46 0.81 2.23

Luxembourg 4.43 3.13 4.94 3.46 4.11 3.09

Malta 3.78 2.79 4.01 3.02 3.49 2.73

Netherlands 2.16 1.95 1.81 1.94 2.24 2

Portugal 2.7 2.59 3.69 2.84 1.9 2

Singapore 6.76 4.05 7.81 4.15 6.04 4

Slovakia 2.46 5.49 2.67 1.21 2.68 6.19

Slovenia 2.44 4.59 2.44 4.59

Spain 2.7 2.07 2.72 2.05 2.64 2.18Switzerland 1.73 1.76 2.38 1.89 1.29 1.59

Taiwan 5.82 3.02 7.7 2.64 4.78 2.84

USA 2.68 2.08 3.05 2.54 2.5 1.9

Non-IT24* 2.96 2.96 3.49 2.16 2.67 2.96

Note:


Excludes Slovenia

Among the IT countries, five countries (Canada, Mexico, Norway, South Korea, Iceland

and Turkey) faced a fall in output growth after IT adoption whereas four countries

(Iceland, Israel, New Zealand and Sweden) experienced an increase in output volatility.

In table 4.5, generally non-IT countries experienced fall in output growth. On average,

output volatility has increased from 2.16% to 2.96% between the pre 1990 and end of

post 1990 period. On the evidence presented in table 4.4, IT in general has not been

associated with increase in output volatility and has been favourable to output growth,

albeit moderately. However as Walsh (2009) mentions the fall in output volatility may


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be associated with good luck view of Great Moderation period. Nevertheless, among

non-IT countries except USA, Switzerland, Singapore, Portugal, Malta, Hong Kong and

Greece experienced increased output volatility. Tables 4.3 to 4.5 suggest that both IT

and non-IT countries central banks placed increased importance on stable and lowinflation over the period 1980 to 2009.

Figure 4.1 and 4.2 depicts the average inflation rates and inflation volatility against

time for both IT and non-IT economies.

The gap between average inflation rates and inflation volatility between IT and non-IT

economies is more pronounced before early 1990s. The gap between these two

measures diminishes during the targeting periods i.e. 1990s and onwards hence

implying monotonic convergence. This reinforces the finding that after the period 1990,

there was a greater aversion among central banks among IT and non-IT economies

towards inflation. Pertaining to inflation, the results so far emphasize that inflation

volatility has fallen with inflation rates for both IT and non-IT countries post 1990.

Figures 4.3 and 4.4 depict the average growth rates and output volatilities averagedover the sample period for both IT and non-IT economies. From figure 4.3, both IT and

0

10

20

30

40

50

60

70

1980 1983 1986 1989 1992 1995 1998 2001 2004 2007

Rate

Year

Figure 4.1: Average Inflation

Non Inflation Targeting

countries

Inflation Targeting countries


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non-IT economies enjoyed periods of favourable growth in 1990s where 11 countries

in the sample adopted IT however both groups faced slump in the early 2000 and

towards the end of the sample. These were the periods where developed economies

suffered recessionary effects due to external shocks. Importantly the average growth

rates of IT countries were closely followed by the average growth rates of non-IT

countries thus suggesting growth behaviour was the same for these groups. Figure 4.4

suggests that both group of countries faced a fall in average output volatilities during

the inflation targeting periods i.e. year 1990 and onwards. However since early 1990s

till the end of the sample output volatility for non-IT economies were confined within

2% to 3%. Hence figure 4.4 suggests that both IT and non-IT countries faced favourable

tradeoffs in terms of inflation and output volatility.

Looking at the data in this way is informative and suggestive, however it is not

conclusive. The above descriptive analyses do not constitute an evidence of causal

relationship between IT and better economic outcomes, is bivariate and it does not

account for changes in other variables that may affect the macroeconomic indicator of

interest. From the above information summarized, IT is associated with lowering of

inflation for all IT countries, but central banks also achieved lower inflation without any

explicit targeting. During the 1990s many countries experienced lower and stableinflation rates due to changes in the structural characteristics in labour markets. Ihrig

0

50

100

150

200

250

1980 1983 1986 1989 1992 1995 1998 2001 2004 2007

StandardDeviationofInflationrate

Year

Figure 4.2: Inflation Volatility


countries

Inflation Targeting

Countries


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and Marquez (2004) finds that among 19 industrialized countries persistent labour

market slack was the main factor exerting downward pressure for inflation in addition

to acceleration in productivity effects for USA. Labour market reforms helped to push

down inflation dramatically in Ireland, Norway and New Zealand.

-6

-4

-2

0

2

4

6

1980 1983 1986 1989 1992 1995 1998 2001 2004 2007Rate

Year

Figure 4.3: Average output growth


countries

Inflation Targeting Countries

0

1

2

3

4

5

6

7

1980 1983 1986 1989 1992 1995 1998 2001 2004 2007

StandardDeviationofoutputgrowth

Year

Figure 4.4: Output Volatility


countries

Inflation Targeting

countries


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Furthermore the decline in output volatility over the most of the course of last two

decades was due to what is called as the Great Moderation Period (Stock and Watson

,2003) and not due to IT itself. Hence this warrants a formal statistical investigation on

the importance of IT on macroeconomic outcomes.

5. METHODOLOGY

For estimating long and short run elasticities researchers often use a form of a

geometric lag model called the partial adjustment model. The following partial

adjustment model is utilized:

(5.1)

where is inflation rate, growth rate, inflation or output growth volatility. Thesubscript indexes country; is the time period. The term is included to capture persistence and mean reverting dynamics and as a consequence

there are time observations for the dependent variable. The main interest is ITdummy variable which will be equal to 1 if country i adopted IT in period tand 0otherwise. Therefore is the treatment variable which measures the average effect ofIT across all IT economies. Vector includes other covariates, some possiblyendogenous. The time or period dummies control for common time or period effectsand capture common shocks to all countries. allows for cross country fixed effectsand is the disturbances. It is assumed throughout that are serially uncorrelated.For concreteness, will be sometimes be mentioned as average inflation and similarlyfor other macroeconomic indicators. is log transformed using .The inflation rate is log transformed to prevent the results from being biased by small

number of countries with high inflation. Another motivation to use this log transform is

that simple log transform to down weight very large readings, over weights readings

that are very close to zero where the log such readings are large negative numbers.

The model (5.1) implies that ordinary least squares (OLS) and fixed effects (FE)

would render biased and inconsistent estimates (Baltagi, 2005; Bond, 2002; Nickell,

1981). The consistency of the FE estimation depends on T being large. However, in

simulation studies, Judson and Owen (1999) found a bias equal to 20% of the coefficient


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of interest even when T = 30. Standard results for omitted variables indicate that at

least in large samples, the FE estimator and OLS are biased downwards and upwards

respectively (Bond, 2002).

Given the possibility of reverse causation on inflation (or other macroeconomic

indicators) on IT, or a third omitted time variant factor causing both IT adoption and

inflation reduction and that both OLS and FE yields biased and inconsistent estimates

provides the motivation to use D-GMM estimation for the model (5.1) that controls for

both simultaneity and omitted variable bias. The D-GMM estimation strategy is due to

Holtz-Eakin et al. (1988) and Arellano and Bond (1991). Under the assumptions that (i)

disturbances are serially uncorrelated, (ii) weakly exogenous explanatory variablesand a mild condition that (iii) initial conditions are predetermined (i.e. not correlated

with future disturbances), D-GMM approach consists of differencing (5.1) to expunge

the country fixed effects and to apply the following moment conditions on

instruments :

(5.2)

where Using these moment conditions, Arellano and Bond(1991) proposes a two-step GMM estimation. In the first step the error terms are

assumed to be homoskedastic and independent across countries and over time. In the

second step, the residuals obtained in the first step are then used to construct a

consistent estimate of the variance-covariance matrix for the second-step estimation,

therefore relaxing the assumptions of independence and homoskedasticity. Thus the

two-step estimation is asymptotically more efficient than one-step even when the

errors are homoskedastic. To correct for the downward bias of two-step standard

errors, the Windmeijers (2005) finite sample correction pro cedure is used to the two-

step estimator variance-covariance matrix. Hence this paper uses only two-step

estimation.

While GMM approaches are more suited to micro data where Nis large relative to T,

it can cause problems in macro data where T is large relative to the number of

countries, N, because the number of instruments, function of T, climbs towards the

number of countries, N. As Roodman (2009) mentions this instrument proliferation


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problem can bias the results by over-fitting the instrumented variables. To deal with

this problem the data is summarized over many 3 year periods as in Islam (1995) and

Acemoglu et al. (2008). Averaging the data over intervals means that results are less

likely to be driven by co-movements at very short horizons, lessens the impact ofmeasurement error and simplifies the specification of the dynamics of the model

(Hwang and Temple, 2005). It is also a good concession between giving enough time for

slow response of macroeconomic variables and isolating the IT treatment effects from

events occurring in close proximity. This allows entering information contained in a

long time series into smaller time periods while holding down the number of

instruments. As mentioned in Roodman (2009), to overcome instrument proliferation

problem and hence over fitting, the dimensionality of the matrix of instruments is

reduced by collapsing its columns. Columns of the instrument matrix embodying the

moment conditions in (5.2) for all tand s are collapsed into a single moment condition

as for all s, as in Calderon et al. (2002).

A potential drawback of D-GMM is that it leads to low precision and finite sample

biases when the time series is a highly persistent process; lagged levels of variables are

poor instruments for first differences (Blundell and Bond, 1998; Bond et al., 2001).

Since it is reasonable that inflation and IT dummy variable are persistent processes

their past values convey little information about future changes and hence provide poor

instruments for the transformed equation in differences. To increase efficiency an

alternative approach, the S-GMM, was suggested by Arellano and Bover (1995) and

Blundell and Bond (1998). To increase efficiency, Blundell and Bond (1998) suggest

also using the moment conditions5:

(5.3)

where the fixed effects are expunged from the instruments using orthogonal deviations

as used by Arellano and Bover (1995) and mentioned in Roodman (2006), and using

these moment conditions with (5.2) in S-GMM approach. Hence S-GMM approach

5 Only the most recent lagged differences are used as instruments. Using other lagged differences in

instruments results in redundant moment conditions given the moment conditions exploited in (5.2) (see

Arellano and Bover, 1995). In other words, lagged two periods or more are redundant instruments,

because corresponding moment conditions are linear combinations of those already in use in (5.2).


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augments the D-GMM approach by using lagged values as instruments for regression in

differences with lagged differences as instruments for regression in levels. That is S-

GMM estimation combines in a system the regression in differences with regression in

levels. The above moment conditions are valid if changes in any instrument areuncorrelated with the fixed effect i.e. for all zand t. In other wordsthere should be no correlation between changes in right hand side variables in (5.1)

with the fixed effects, but there may be correlation in levels. Sufficient conditions for

this are that (iii) which is the initial condition and (iv) conditional oncommon time effects, the first moments of and are invariant of time or and . As mentioned above it is assumed thatdisturbances are serially uncorrelated. The assumption on the initial condition givenin (iii) holds when the initial condition satisfies mean stationary assumption6. Loosely

speaking countries in the sample are in steady state in this sense that deviations from

long term values after controlling for covariates are not systematically related to fixed

effects. This prescribes that IT adoption is not correlated to the inflation fixed effects,

however IT regime can have time invariant relation i.e. , where a forall t and IT adoption to be related to changes in inflation, andsimilarly for . To prevent the problem of instrument proliferation and biasing theresults the columns of matrix of instruments for S-GMM is collapsed as mentioned

earlier.

Blundell and Bond (1998) show using Monte Carlo studies for the case of AR(1)

specification that S-GMM can lead to dramatic reductions in finite sample bias and

efficiency gains for small Tand persistent series. The results are also corroborated by

Hahn (1999), Blundell and Bond (2000) and Blundell et al. (2002). Soto (2009) usingMonte Carlo simulations found that provided that some persistence is found in the data,

S-GMM outperforms D-GMM when N is small i.e. S-GMM has a lower bias and a higher

efficiency. This is especially important for macro data or in empirical growth literature

when N, the number of countries is small and size ofTis moderate.

To test the validity and consistency of the GMM, specification tests are employed as

mentioned in Arellano and Bond (1991). The consistency of the GMM estimators

6See Blundell and Bond (1998) for details on this assumption.


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presented above relies that there is no second-order serial correlation in the first

differenced disturbances, . But by construction might be first-order seriallycorrelated even if is not. The additional moment conditions are over identifying

restrictions and to test their validity, tests of over indentifying restrictions are used. Totest the validity of additional restrictions for D-GMM and S-GMM, Hansens (1982)Jtest

is used and to test the additional moment conditions that are used for regression in

levels in S-GMM, difference in Hansen C test is used. This tests statistic tests for the

validity of subsets of instruments used for equation in levels whereas the Hansen Jtest,

tests the overall validity of instruments. To overcome the weaknesses of tests of over

identifying restrictions due to instrument proliferation the size of the matrix of

instruments is collapsed as mention above. Sargan and difference in Sargan tests are notvulnerable to instrument proliferation but they require homoskedastic errors for

consistency which is rarely assumed (Roodman, 2009). If Hansen J and Ctest statistic

rejects the null of validity of moment conditions and additional moment conditions as in

(5.2) and in (5.3) then this implies endogeneity of some the instruments used. If the

above tests fail to reject the null, then this lends support to the model, validity of

moment conditions and its specification.

6. RESULTS

6.1. PRELIMINARY RESULTS

Tables 6.1 and 6.2 present various estimates of the following equation:

(6.1)

where is the macroeconomic indicator of interest. and are common time effectand the country fixed effects respectively. The main interest is the IT dummy variable

which is equal to 1 if country i is an inflation targeter in period tand 0 otherwise.To prevent bias in the favour of the IT dummy, high inflation dummy is partially

controlled using the dummy which is equal to 1 if average inflation is greaterthan 0.20 per year (in natural logarithm) in period tand 0 otherwise. As in the spirit of

Ball and Sheridan (2005) is output growth or output volatility to find if IT had anyeffects on the real economy. It is sensible to keep when assessing the impacts of


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IT on real economy as Bruno and Easterly (1998) and Barro (1996) have recognized

differences in growth pattern during high inflationary periods.

Table 6.1: Estimates of Inflation targeting effects on inflation and output growth (1980-2009)

Estimator: TE-OLS WG D-GMM P D-GMM E S-GMM P S-GMM E

Regressors: (1) (2) (3) (4) (5) (6)

6.1.A- Inflation equation

Inflation targeting dummy 0.67 -0.28 -3.54 -3.72 1.77 0.85

(0.12) (0.92) (0.66) (0.73) (0.17) (0.24)

Lagged inflation 0.21 0.1 -0.01 -0.01 -0.08 -0.09

(0.01) (0.28) (0.97) (0.92) (0.62) (0.58)

High inflation dummy 36.7 37.6 57.9 61.5 71.5 72.8

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)AR(1) test 0.09 0.12 0.06 0.06

AR(2) test 0.13 0.18 0.12 0.13

HansenJtest 0.51` 0.18 0.09 0.07

Difference-in-Hansen 0.59 0.81

Observations 340 340 301 301 340 340

Instrument columns 29 28 33 32

R-squared 0.59 0.44

6.1.B Output growth equation

Inflation targeting dummy 0.18 -0.01 -1.10 -1.32 0.71 0.57(0.43) (0.99) (0.69) (0.66) (0.02) (0.11)

Lagged output growth 0.41 0.14 0.25 0.24 0.30 0.30

(0.00) (0.01) (0.03) (0.03) (0.00) (0.00)

High inflation dummy -1.49 -3.28 -2.60 -2.82 -1.38 -1.42

(0.10) (0.00) (0.29) (0.27) (0.20) (0.22)

AR(1) test 0.00 0.00 0.00 0.00

AR(2) test 0.42 0.48 0.41 0.41

HansenJtest 0.20 0.16 0.28 0.23


Observations 343 343 304 304 343 343Instrument columns 29 28 33 32

R-squared 0.38 0.38

p-values in parentheses. AR(1), AR(2), HansenJtests, and difference-in-Hansen report the respectivep-

values.

(1)-(2) uses robust standard errors clustered by country.

(3)-(6) uses Windmeijer's (2005) corrected standard errors.

Data averaged over three year period.

In columns (4) and (6) IT is endogenous (E) whereas in (3) and (5) it is predetermined (P).


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Column (1) of tables 6.1 and 6.2 presents pooled OLS results with time effects where

standard errors are clustered by country. The time effect captures the worldwide trend

events and productivity changes common to all countries. Results show that IT has been

ineffective in reducing inflation and inflation volatility (tables 6.1.A and 6.2.A) which aretwo main goals of the central bank. On contrary IT is shown to have positive effects on

output growth and growth volatility with an estimated per year impact of 0.18% and -

0.08% respectively (tables 6.1.B and 6.2.B) but the results are insignificant. Hence OLS

presents that IT has been unsuccessful in reducing inflation and inflation volatility.

Rather the positive sign indicates that it produced adverse effects on these two

variables which are key policy variables for central bank but the effects are insignificant.

Column (2) of tables 6.1 and 6.2 present the Within Group (WG) or FE estimates where

estimation indicates that IT has favourable impact on inflation with adverse costs in

terms of output growth (tables 6.1.A and 6.1.B) and was ineffective in stabilization of

inflation and output (tables 6.2.A and 6.2.B) but results are largely insignificant.

However as mentioned above both estimations are biased and inconsistent and WG

suffers from dynamic panel bias (Nickell, 1981) where the direction of the bias for OLS

and WG is upwards and downwards respectively. Thus if there is a candidate consistent

estimator, it is expected that it will lie between OLS and WG estimates.

The two-step D-GMM estimates presented in columns (3) and (4) of tables 6.1 and

6.2 fixes the dynamic panel bias and takes into account the undisputable endogeneity

of . For t3 column (3) uses the instruments ( ) forj=0,1,,t-3 (for predetermined IT) and column (4) uses ( )for j=0,1,,t-3 where IT is treated as endogenous. As mentioned earlier the matrix of

instruments is collapsed to prevent over fitting problem. D-GMM estimates in columns

(3) and (4) indicate that IT has positive impacts on inflation but coming at the cost of

lower output growth (tables 6.1.A and 6.1.B). There is no indication IT has been

successful in lowering macroeconomic volatilities (tables 6.2.A and 6.2.B). However

none of the results are significant. The GMM specification tests 7 also do not indicate a

problem of serial correlation of residuals using the AR(1) and AR(2) test statistics in

tables 6.1 and 6.2. As mentioned earlier that consistency of the GMM estimates crucially

7For details of the specification tests see Arellano and Bond (1991), Hayashi (2000) and Roodman (2006)


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depends on i.e. no second-order serial correlation for thedisturbances in the first

Table 6.2: Estimates of Inflation targeting effects on macroeconomic volatility (1980-2009)


Regressors: (1) (2) (3) (4) (5) (6)

6.2.A- Inflation volatility equation

Inflation targeting dummy 0.44 2.15 4.97 3.88 -0.54 1.30

(0.12) (0.55) (0.48) (0.66) (0.62) (0.16)

Lagged inflation volatility 0.201 0.05 0.168 0.166 -0.068 -0.066

(0.05) (0.64) (0.33) (0.35) (0.82) (0.83)


(0.02) (0.02) (0.08) (0.08) (0.06) (0.05)

AR(1) test 0.199 0.20 0.22 0.23

AR(2) test 0.24 0.25 0.16 0.17

HansenJtest 0.02 0.02 0.02 0.01


Observations 340 340 301 301 340 340


R-squared 0.3 0.25

6.2.B Output growth volatility equation

Inflation targeting dummy -0.08 0.21 1.73 1.72 0.06 0.05

(0.68) (0.41) (0.24) (0.26) (0.78) (0.86)

Lagged output growth volatility 0.23 0.02 0.13 0.13 0.13 0.33

(0.00) (0.68) (0.06) (0.07) (0.05) (0.05)


(0.00) (0.00) (0.13) (0.15) (0.02) (0.02)

AR(1) test 0.00 0.00 0.00 0.00

AR(2) test 0.87 0.88 0.96 0.95

HansenJtest 0.72 0.70 0.29 0.24


Observations 342 342 303 303 342 342


R-squared 0.38 0.38


values.

(1)-(2) uses robust standard errors clustered by

country.





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differenced equation which tests for serial correlation for disturbances in levels

(Roodman, 2006). The Hansen Jtest which tests the overall validity of the instruments

is also not rejected in tables 6.1.A, 6.1.B and 6.2.B. However D-GMM results are

disappointing. It is well documented for example in Blundell and Bond (1998) that D-GMM suffers from weak instrument problems due to series being highly persistent or

closely following a random walk process and hence D-GMM performs poorly (leads to

finite sample bias) in terms of precision. Therefore past levels of variables provide weak

instruments (becomes less informative) for equation in differences for D-GMM. Output

is a persistent process and as mentioned earlier so are inflation and IT dummy

variables.

To increase efficiency a more appropriate approach of S-GMM due to Arellano and

Bover (1995) and Blundell and Bond (1998) is used which exploits additional moment

restrictions as mentioned above. Columns (5) and (6) produce the two-step S-GMM

estimates. For t3 column (5) of tables 6.1 and 6.2 use the following instruments

( ) for j=0,1,,t-3 for equations in differences and theinstruments ( ) for the equation in levels where IT ispredetermined. In column (6) of tables 6.1 and 6.2, IT variable is treated as endogenous

to address possible reverse causality from inflation and/or output growth to IT.

Alternatively to take into account a third country specific time varying factor that

simultaneously determines both the macroeconomic performance and the monetary

policy. Then for t3, the following instruments ( ) forj=0,1,,t-3 for equations in differences and the instruments ( )for the equation in levels are used.

S-GMM estimates confirm the weak instruments problem of D-GMM estimates intables 6.1.A, 6.1.B, 6.2.A and 6.2.B. For instance relative to D-GMM estimates in columns

(3) and (4) for inflation equation in 6.1.A, the IT coefficient becomes positive and

weakly significant at 20% to 25% for S-GMM estimates in columns (5) and (6)

indicating that IT did not produce favourable effects on inflation IT economies were

not successful in reducing the inflation rates relative non-IT economies. In column (5) in

6.1.A IT imposes a negative impact of 1.77% per year on inflation rate.


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Columns (5) and (6) in contrast to D-GMM estimates in (3) and (4) of table 6.1.B also

confirms the weak instrument problem (output is persistent process as indicated by

lagged output growth coefficient i.e. 1-0.25=0.75 as in column (3) table 6.1.B or 1-

0.24=0.76 as in column (4)) as S-GMM estimates show that IT produced higher outputgrowth relative non-IT economies and results are marginally significant. Thus inferring

the S-GMM estimate when IT is endogenous from column (6) in table 6.1.B for instance,

IT had a positive impact on output growth of magnitude 0.57% per year at 15%

significance. This hints that central banks have been more flexible with IT policy and

placed relatively greater weight towards growth.

S-GMM estimates indicate that IT was not effective at stabilizing inflation and output

but the results are largely insignificant (tables 6.2.A and 6.2.B). Again as for output

growth in table 6.1.B there is weak instrument problem for D-GMM estimates in table

6.2 especially for output volatility in 6.2.B. As pointed out by Spilimbergo (2009),

another way to identify the persistence of the series and detect/diagnose weak

instrument problem is to consider the differences in coefficient estimates of OLS, WG

and unbiased GMM estimator. In column (1) table 6.2.B for example, OLS provides an

estimate of -0.08% per year impact of IT on output volatility and in column (2) WG

provides 0.21% whereas an unbiased GMM estimate in column (6) of table 6.2.B yields

0.05%. This technique along with comparing S-GMM estimates relative to D-GMM

estimates and/or computing , as done for output growth in the precedingparagraph revels the persistence of the series and the nature of the weak instrument

problem. The S-GMM estimate in column (6) where IT is treated as endogenous in tables

6.1.A and 6.2.A reveals the simultaneity existent between inflation, inflation volatility

and IT regime as indicated by large changes in magnitude and direction of the

coefficient estimates, thus indicating IT is influenced by the average inflation and

inflation volatility error, cov . The S-GMM estimates of output and outputvolatility in tables 6.1.B and 6.2.B do not change much in magnitude and in direction

thus suggesting that main cause of endogeneity bias is reverse causality from inflation

and its volatility to IT.

The specification tests do not reject the S-GMM estimates for output and output

volatility (tables 6.1.B and 6.2.B). Another evidence of consistency is that both lagged

output and output volatility GMM estimates are between the OLS and WG estimates. On


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a worrying note Hansen Jtest is weakly insignificant for the average inflation equation

for the S-GMM estimates in columns (5) and (6) of table 6.1.A. However the test statistic

rejects the validity of the overall instruments for the inflation volatility equation in 6.2.A

for both D-GMM and S-GMM specifications. Furthermore the difference in Hansen Ctest,which tests the validity of the additional moment conditions used in S-GMM,

or the exogeneity of the extra lagged instruments in levels is rejectedfor inflation volatility equation at 1% in table 6.2.A in columns (5) and (6). Hence the

efficiency gain from S-GMM is not free; we need extra assumptions and the violation

which leads to bias. The weak exogeneity of some of instruments as indicated by Hansen

Jtest in table 6.1.A for inflation raises some concerns and doubt regarding the S-GMM

estimates. However D-GMM estimates in columns (3) and (4) remain consistent andindicates that IT has -3.72% per year impact on inflation rate taking into account the

endogeneity of IT8 with estimated long run effect of -3.68% ( ), however it isinsignificant. In columns (5) and (6) of table 6.1.B, S-GMM estimates show IT had

significant or marginal significant effect on output growth where the impact per year

lying in 0.57% to 0.71% range. If the lagged coefficient which controls for mean

reversion or regression to mean is significant and between 0 and 1 and IT dummy

coefficientis insignificant then it implies that countries that had higher inflation saw agreater decline in inflation than already low inflation countries. Similar analogy also

applies to output and volatilities. In contrast to simple regression to mean evidence

found in Ball in Sheridan (2005) for inflation, table 6.1.A for inflation does not indicate

this is the case. Thus the significant or marginal significant IT impact on output growth

is not due to simple regression to mean but for output growth volatility it is (columns

(5) and (6), tables 6.1.B and 6.2.B).

The high inflation dummy also provides interesting results it significantly affects

inflation and promotes greater volatilities in the economy hence suggesting that in high

inflation periods macroeconomic indicators have different long run means. Also as

expected high inflation has a negative impact on growth confirming the findings that

countries going through high inflation grew less (Bruno and Easterly, 1998) but results

are not significant.

8Uhlig (2004) mentions that choice of IT has been an endogenous one by the countries that has adopted it.


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6.2. 1985-2002

To examine the sensitivity of the above results to different sample period, the period

1985-2002 is chosen9. In columns (5) and (6) of table 6.3.A the difference in Hansen C

test rejects the validity of additional moment conditions for S-GMM estimates for the

inflation equation. The test statistic weakly rejects (at 10%) the extra instruments in

levels for the S-GMM when IT is treated as predetermined variable but when IT is

endogenous it is rejected at 5%. However the D-GMM estimates in columns (3) and (4)

are still valid according to the specification tests and are consistent but it is not efficient.

D-GMM estimates are still valid if one is unwilling to accept the condition of Blundell

and Bond (1998) that . Hence on the face of it, IT has been successful inreducing inflation at marginal significance level (10% or 15%) where it has -7.97% to -

8.34% per year impact on the inflation rate beyond simple regression to mean i.e. even

after taking into account lagged inflation. There may be indication of endogeneity as the

coefficient estimate in (4) is more negative.

The D-GMM estimates in columns (3) and (4) of table 6.3.B indicate that IT has been

adverse for output growth for the 1985-2002 period imposing a significant negative

cost of -7.97% to -8.42% per year impact. Nevertheless as in table 6.1.B, the S-GMM

estimates reveal weak instrument problem of D-GMM results in table 6.3.B for output

equation. S-GMM estimates indicate that IT did not have any significant impact on

output growth for IT economies. The S-GMM results in columns (5) and (6) of table

6.3.B also reveals the importance of taking into account the endogeneity of IT as the

magnitude of IT per year impact on output growth estimate changes from 0.24% to

0.09% in table 6.3.B for the output growth equation. Hence the S-GMM estimates in

columns (5) and (6) are preferred results and they are fairly robust to sample periods in

a sense that IT is not found to have any adverse impact of output growth and

furthermore the specification tests do not reject the validity of the instruments used and

consistency. But now there is some evidence of simple regression to mean effect. Lagged

output growth estimates in table 6.3.B also lie between OLS and WG estimates further

evidence of consistency.

9This period was also used by Wu (2004) and Willard (2006).


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Table 6.3: Estimates of Inflation targeting effects on inflation and output growth (1985-2002)


Regressors: (1) (2) (3) (4) (5) (6)

6.3.A Inflation equation

Inflation targeting dummy 1.06 -0.50 -7.97 -8.34 -4.97 -1.33

(0.35) (0.86) (0.08) (0.15) (0.55) (0.41)

Lagged inflation -0.04 -0.26 0.22 0.23 0.29 0.36

(0.82) (0.10) (0.03) (0.00) (0.32) (0.43)

High inflation dummy 50.70 44.40 3.25 2.70 -10.70 -21.50

(0.00) (0.04) (0.81) (0.83) (0.81) (0.75)

AR(1) test 0.14 0.16 0.46 0.53

AR(2) test 0.18 0.70 0.68

HansenJtest 0.92 0.86 0.33 0.15

Difference-in-Hansen 0.07 0.02Observations 190 190 151 151 190 190


R-squared 0.52 0.53


Inflation targeting dummy 0.05 0.04 -8.42 -7.85 0.24 0.09

(0.88) (0.96) (0.05) (0.03) (0.55) (0.86)

Lagged output growth 0.40 -0.01 0.15 0.17 0.40 0.30

(0.00) (0.84) (0.51) (0.35) (0.02) (0.14)

High inflation dummy -1.69 -4.39 -15.70 -12.6 0.20 0.26(0.03) (0.00) (0.12) (0.13) (0.98) (0.97)

AR(1) test 0.12 0.08 0.02 0.05

AR(2) test 0.64 0.49 0.71 0.73

HansenJtest 0.78 0.84 0.61 0.46


Observations 192 192 153 153 192 192


R-squared 0.31 0.28


values.

(1)-(2) uses robust standard errors clustered by country.




The endogeneity bias as well as the weak instruments problem is also apparent from

estimates in tables 6.4.A and 6.4.B for inflation and output volatilities respectively. S-

GMM estimates taking into account endogeneity of IT in column (6) of tables 6.4.A and


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6.4.B indicate that IT was favourable in reducing macroeconomic volatility but did not

have any significant effects.

Table 6.4: Estimates of Inflation targeting effects on macroeconomic volatility (1985-2002)


Regressors: (1) (2) (3) (4) (5) (6)

6.4.A Inflation volatility equation

Inflation targeting dummy 1.17 -0.07 1.66 1.38 0.32 -0.10

(0.12) (0.96) (0.62) (0.68) (0.57) (0.90)

Lagged inflation volatility -0.02 -0.11 0.04 0.02 0.08 0.08

(0.73) (0.23) (0.58) (0.78) (0.25) (0.26)


(0.05) (0.03) (0.33) (0.30) (0.63) (0.77)AR(1) test 0.80 0.65 0.46 0.42

AR(2) test 0.62 0.59 0.28 0.18

HansenJtest 0.21 0.49 0.18 0.12


Observations 189 189 150 150 189 189


R-squared 0.27 0.23

6.4.B Output growth volatility equation

Inflation targeting dummy 0.04 -0.25 2.31 2.30 0.23 -0.13(0.88) (0.53) (0.53) (0.35) (0.66) (0.80)

Lagged output growth volatility 0.13 -0.21 -0.21 -0.32 -0.06 -0.11

(0.05) (0.07) (0.26) (0.22) (0.20) (0.15)


(0.00) (0.00) (0.16) (0.02) (0.07) (0.01)

AR(1) test 0.01 0.01 0.01 0.01

AR(2) test 0.69 0.76 0.85 0.72

HansenJtest 0.21 0.48 0.28 0.31


Observations 192 192 153 153 192 192


R-squared 0.28 0.31


values.(1)-(2) uses robust standard errors clustered by

country.





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The difference in Hansen C tests however rejects the additional assumptions made or

additional moment restrictions used in S-GMM at 5% level for the inflation volatility

equation in column (6) of table 6.4.A where IT is treated as endogenous variable. In this

event the D-GMM estimates are again consistent and are accepted by specification testshowever they suggest that IT was ineffective in reducing macroeconomic volatilities but

results are once again not significant at any reasonable significance level. The

specification tests do not reject the moment conditions for output growth volatility

equation in 6.4.B and no second-order serial correlation is found. Hence for output

volatility in table 6.4.B the additional instruments seem to be valid and highly

informative. Furthermore lagged output growth volatility coefficients lies in between

OLS and WG estimates further evidence of consistency. Once again results are robust

i.e. IT is not found to have any significant adverse impact on inflation volatility as well

output growth volatility across the two sample periods.

6.3. ROBUSTNESS ANALYSIS

So far, the empirical evidence showed that IT didnt have any significant or adverse

effects on macroeconomic volatility (tables 6.2.B and 6.4.B). There is indication that IThad favourable impact in reducing inflation using D-GMM estimates but not with S-GMM

estimation which is expected to be more efficient however specification tests are

against the S-GMM results as found above (table 6.3.A). IT was shown to have positive

significant impact on output growth but for the period 1985-2002 it due to mean

reversion. An important question is that are these results robust?

To further test the sensitivity of the results, reduced sets of instruments are used

where only until lag 3 instruments are used. As Roodman (2009) mentions it is

important to always check for robustness of the analysis using reduced instruments.

Different IT adoption dates are also materialized10 using reduced instrument sets to

further check for sensitivity. For Chile, Czech Republic, Israel and Mexico IT adoption

dates according to Batini and Laxton (2007) are used. For Australia, Canada, Finland,

10When using full set of instruments for different IT dates, conclusions regarding IT effects do not change

significantly but some specification tests are not supportive regarding the validity of the models hence reduced

set of instruments are used for different IT adoption dates analysis.


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New Zealand and UK adoption dates for constant IT11 i.e. meaning unchanging target or

target range. The results are presented for S-GMM only12 as Hayakawa (2005) finds

analytically and experimentally that despite using more instruments S-GMM is more

efficient than D-GMM.

In columns (1)-(4) of table 6.5.A the S-GMM estimates of the effects of IT on inflation

is negative and now mostly significant implying that it had adverse effects on inflation.

Results are robust when using reduced instruments as in columns (1) and (2) and with

different IT adoption dates in (3) and (4). Consistent with above findings for the sample

period 1985-2002, in columns (5) and (6) of table 6.5.A IT produces positive but

insignificant impact in reducing inflation when used with reduced instruments. Hence

there is a paradox i.e. IT was largely ineffective in reducing inflation, but there is some

evidence, albeit weak that it had a positive impact on inflation. This may indicate

multiple hypotheses. In 1980-2009, IT regime has been increasingly flexible and

discretionary (pursuing expansionary polices) compared to 1985-2002 period giving

more weight to output growth. A closer examination of figure 3.1 in section 3 reveals

that post 2002 both IT and non-IT economies at low levels experienced rising inflation.

Secondly, announcement of a formal inflation target was not successful in anchoring

publics expectations of inflation and to mimic policy under commitment thus failing to

establish credibility (see section 2 on theory) therefore unable to produce lower

inflation rates. A third view is that central banks during the last few years have pursed

discretionary monetary or fiscal policies to prevent the spread of deflationary

expectations that may have been present in the period 1985-2002. But overall at face

value and generally, the IT results for average inflation from tables 6.1.A, 6.3.A and 6.5.A

indicates that it has largely been unsuccessful in reducing in inflation and this abides

with the results found in Ball and Sheridan (2005) and Willard (2006) but results are

fairly robust for its adverse effects on inflation. However there is some evidence that IT

matters for inflation according to D-GMM estimates in columns (3) and (4) table 6.3.A

but as mentioned earlier they may be severely biased due to weak instruments problem.

Either central banks were not able anchor inflation expectations and thus establish

credibility or they were too flexible. This clearly implies two things. Firstly, given the

11See Ball and Sheridan (2005) for constant IT.

12 Results for D-GMM were also carried out but in all cases they more inefficient relative to S-GMM indicating

the weak instrument problem.


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Table 6.5: Estimates of Inflation targeting effects on inflation and output growth, robustness

checks

Different IT adoption

dates and reduced

instruments 1980-

2009

Reduced instruments

1980-2009

Reduced instruments

1985-2002

Estimator: S-GMM P S-GMM E S-GMM P S-GMM E S-GMM P S-GMM E

Regressors: (1) (2) (3) (4) (5) (6)

6.5.A Inflation equation

Inflation targeting dummy 2.10 1.29 2.15 1.06 -0.51 -0.92

(0.00) (0.04) (0.01) (0.17) (0.84) (0.83)

Lagged inflation -0.11 -0.13 -0.09 -0.15 0.36 0.36

(0.48) (0.46) (0.59) (0.48) (0.19) (0.13)

High inflation dummy 65.90 63.70 58.40 62.20 -21.70 -21.71

(0.00) (0.00) (0.03) (0.04) (0.53) (0.52)

AR(1) test 0.08 0.10 0.12 0.11 0.41 0.33

AR(2) test 0.13 0.14 0.16 0.17 0.56 0.49

HansenJtest 0.69 0.74 0.39 0.20 0.61 0.50

Difference-in-Hansen 0.64 0.70 0.23 0.20 0.27 0.18

Observations 340 340 340 340 190 190

Instrument columns 18 18 18 18 14 14


Inflation targeting dummy 0.25 0.16 0.03 0.35 0.34 0.14

(0.48) (0.68) (0.92) (0.42) (0.60) (0.72)

Lagged output growth 0.39 0.39 0.40 0.40 0.51 0.63

(0.10) (0.10) (0.00) (0.00) (0.09) (0.14)

High inflation dummy -0.16 0.11 0.22 0.23 3.48 6.16

(0.89) (0.93) (0

Documents

Appraising Inflation Targeting. Panel Evidence From Developed Economies