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APPRAISING INFLATION TARGETING: PANEL EVIDENCE FROM DEVELOPED ECONOMIES NAME OF AUTHOR: MANHAL M ALIA THESIS SUBMITTED TO THE UNIVERSITY OF BRISTOL IN ACCORDANCE WITH THEREQUIREMENTS OF THE DEGREE OF MSc ECONOMICS IN THE FACUALTY OF SOCIAL SCIENCES AND LAW DEPARTMENT OF ECONOMICS, UNIVERSITY OF BRISTOL SEPTEMBER, 2011 ii
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AbstractBy using dynamic panel GMM techniques this paper finds that in general that inflationtargeting (IT) regime has not led to improvement or was positively effective in terms ofmacroeconomic performance in terms of inflation, output growth, inflation volatilityand output volatility. Hence reinforcing, in summary IT was mainly ineffective. There issome evidence IT had positive impact on inflation, inflation volatility and output growthbut it is not robust and not general. At best there is no indication that IT had adverseeffects on economic stabilization or volatility. There is also no conclusive evidence thatIT has worsened or led to more favourable tradeoffs between inflation and economicactivity. The general results of this paper also align with results of some previousresearches in this field. iii
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WORD COUNTNumber of pages: 60Number of words: 14,992 (including title, abstract and pages 1 to 44 only). iv
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ACKNOWLEDGMENTSI have benefited from the discussions that I had with my thesis supervisors Dr. HeleneTuron and Professor Fabien Postel-Vinay and suggestions that I have received fromthem. My sincere recognition goes to them. I would like to specially thank Dr. HeleneTuron and my academic supervisor Professor Simon Burgess for their kind support tohelp me carry out this thesis. I would also like to thank Professor Jon Temple for kindlymaking one of his papers available to me in order to read on applied work using panelGMM. I gratefully acknowledge the help I have received from thesis help desk regardingthe use of Stata software from Jake Bradley, a senior PhD student at the Department ofEconomics, University of Bristol.Lastly, I would like to dedicate this work to my parents who were extremely supportiveall the way from the beginning. It would have not been possible without them. v
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AUTHOR’S DECLARATIONI declare that the work in this dissertation/thesis was carried out in accordance withthe regulations of the University of Bristol. The work is original except where indicatedby special reference in the text and no part of the thesis has been submitted by otherdegree.Any views expressed in the thesis are those of the author and in no way represent thoseof the University of Bristol.The thesis has not been presented to any other University for examination either in theUnited Kingdom or overseas.SIGNED: DATE: vi
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TABLE OF CONTENTS1. INTRODUCTION ................................................................................................................................................ 12. INFLATION TARGETING IN THEORY ...................................................................................................... 23. PREVIOUS STUDIES ........................................................................................................................................ 64. DATA ....................................................................................................................................................................... 95. METHODOLOGY.............................................................................................................................................. 176. RESULTS ............................................................................................................................................................ 21 6.1. PRELIMANARY RESULTS ............................................................................................................... 21 6.2. 1985-2002 ........................................................................................................................................... 28 6.3. ROBUSTNESS ANALYSIS .................................................................................................................... 31 6.4. INFLATION-OUTPUT TRADEOFF .................................................................................................... 377. LIMITATIONS AND EXTENSIONS ......................................................................................................... 418. CONCLUSION ................................................................................................................................................... 43REFERENCES ....................................................................................................................................................... 45LIST OF FIGURES 4.1. AVERAGE INFLATION ....................................................................................................................... 14 4.2. INFLATION VOLATILITY................................................................................................................... 15 4.3. AVERAGE OUTPUT GROWTH .......................................................................................................... 16 4.4. OUTPUT VOLATILITY ........................................................................................................................ 16LIST 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 vii
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6.2. ESTIMATES OF INFLATION TARGETING EFFECTS ON MACROECONOMIC VOLATILITY(1980-2009)................................................................................................................................................ 246.3. ESTIMATES OF INFLATION TARGETING EFFECTS ON INFLATION AND GROWTH (1985-2002) ............................................................................................................................................................ 296.4. ESTIMATES OF THE INFLATION TARGETING EFFECTS ON MACROECONOMIC VOLATILITY(1985-2002)................................................................................................................................................ 306.5. ESTIMATES OF INFLATION TARGETING EFFECTS ON INFLATION AND GROWTH,ROBUSTNESS CHECKS............................................................................................................................... 336.6. ESTIMATES OF INFLATION TARGETING EFFECTS ON MACROECONOMIC VOLATILITY,ROBUSTNESS CHECKS............................................................................................................................... 356.7. ESTIMATES OF INFLATION TARGETING EFFECTS ON COEFFICIENT OF VARIATIONS OFINFLATION AND OUTPUT GROWTH, ROBUSTNESS CHECKS ............................................................. 366.8. ESTIMATES OF INFLATION TARGETING EFFECTS ON INFLATION-OUTPUT TRADEOFF(1980-2009)................................................................................................................................................ 386.9. ESTIMATES OF INFLATION TARGETING EFFECTS ON INFLATION-OUTPUT TRADEOFF(1985-2002)................................................................................................................................................ 396.10. ESTIMATES OF INFLATION TARGETING EFFECTS ON INFLATION-OUTPUT TRADEOFF,ROBUSTNESS CHECKS............................................................................................................................... 40 viii
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1. INTRODUCTIONOne of the central objectives of the central banks worldwide is to promotemacroeconomic stability by stabilizing and lowering inflation. Several economies,industrial and emerging markets implemented various monetary policy regimes toachieve this objective. A regime that has received significant attention recently isInflation Targeting. It was first pioneered and adopted by New Zealand in 1990. Inrecent years there has been increasing number of countries that adopted inflationtargeting to help to stabilize inflation and promote economic stability. But has inflationtargeting been successful as a monetary policy regime to achieve the aforementionedobjective and in terms of general macroeconomic performance? Certainly from the databoth developed and emerging economies saw reductions in inflation rates sinceadopting this monetary regime. But countries that did not adopt this regime alsoexperienced fall in inflation rates. So did inflation targeting lead to fall in inflation ratesfrom point of view of formal statistical analysis? Did inflation targeting producedsmaller costs in terms of output, was IT favourable to the real economy and managed toreduce volatility? Since its introduction there has been a surge in research on inflation targetingconcerning its effectiveness. So far, the empirical results on this topic are mixed andinconclusive. Results vary according to the methods used and samples selected.Nevertheless this area still remains active an area of research and is debated inacademia and central banks worldwide. The objective of this paper is to enter thisdebate and to answer the questions posed at the beginning i.e. whether inflationtargeting countries benefited in terms of key macroeconomic performance, usingdynamic 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 assesswhether inflation targeting was effective or improved the macroeconomic performanceof developed economies. In general there is no evidence that inflation targetingmattered or in other words inflation targeting was not found to be positively effective. 1
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The paper is divided into eight sections. After this introduction, section two looksbriefly at the theory. Section three present reviews previous literature along with thecontribution of this paper. Section four is concerned with the data and descriptiveanalysis. Section five presents the methodology. Section six presents the econometricresults. Section seven considers extensions and limitations. Section eight concludes.2. INFLATION TARGETING IN THEORYInflation targeting (henceforth IT) is a monetary policy framework where the soleobjective of the central bank adopting it is to promote price stability by committingitself to achieve an explicit target or range for inflation rate by using interest rates orother monetary options. The objective function facing a central bank operating under ITregime 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 impliesthat central bank cannot act to shocks at time t contemporaneously. Information aboutshocks at time t will affect the choice of , and . The central bank’s objectiveis to then minimize (2.2) by choosing : (2.2)where the subscript on the expectations operator is now t-1 to reflect that informationavailable to central bank when it sets its policy, where the constraints are given by ISand New Keynesian Phillips curve given in equations (2.3) and (2.4) respectively: 2
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(2.3) (2.4)where the cost shock et-1 follows an AR (1) process. The first order condition underdiscretion1 for central bank’s 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 t will exceed the targetrate 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 amedium term target for inflation which is usually quite low (usually specified as a fewrange of percentage points). (2) Institutional commitment to price stability as the chieflong run monetary policy goal. (3) Increased transparency through communication withpublic and markets about the monetary policy objectives. (4) Increased accountabilityof 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 itsolves the inflation bias problem due to dynamic inconsistency theory of inflation(Kydland and Prescott, 1977) thus leading to lower inflation rates. Again due to thepolicy being transparent and credible it is understood by the public and therefore it cananchor the expected inflations and can “lock in” expectations of low inflation whichhelps to contain the possible inflationary impact of macroeconomic shocks. Also in thespirit of Barro and Gordon’s (1983) reputation model, central banks can establish a1 Under discretion the policy maker or the central bank chooses inflation taking expectations of inflation asgiven 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 firstorder conditions. (2.5) can be derived by differentiation of the discretionary monetary policy problem withrespect to and and then combining them into one equation. See page 361 of Walsh (2010). 3
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reputation of being tough against inflation in the context of infinitely repeated gameswhere subgame perfect Nash equilibrium exists with inflation lower than discretionaryinflation. By anchoring expected inflations towards the target range, IT can reduce the impactof 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 , centralbank’s implementing as the target also brings about reduced output variability i.e.central bank also cares about output stabilization. Since inflation target is a mediumterm objective where the central bank’s target inflation over a certain horizon and giventhat inflation cannot be controlled instantaneously, short term deviations from thetarget are acceptable and do not necessarily translate into losses in credibility. Thisincreased flexibility also leads to lower output variability. By maintaining low inflationand inflation volatility, IT also helps to promote output growth (Mishkin, 1999). Alsotwo channels in which IT can lead to output growth is through productivity enhancingand finance growth nexus (Mollick et al. 2011). That is transparency, credibility andcertainty associated with IT can lead to better financial sector developments, moredomestic and foreign investments which in turn help to promote growth. However IT has its disadvantages and hence beneficial claims made by its advocatesare rebutted. Critics argue that due to increased weight on inflation it offers littlediscretion and this rigidity unnecessarily restrains growth and increases outputvolatility. Also since targets can be changed and since it offers too little discretion, ITcannot anchor expected inflations. For inflation to be successful the central bank mustdemonstrate its commitment to low and stable inflation through tangible actions. In theinitial periods after adoption, to establish this reputational equilibrium of being toughagainst inflation will require aggressive measures and extra conservatism which willharm output growth. Generally IT constrains discretion inappropriately; it is tooconstrictive (see loss function (2.2)) in terms of ex ante commitment to a particularinflation 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 hitthe target very restrictively. Furthermore there are measurements and implementationissues, 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 4
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and low inflation target rates for example 1% can lead economies to hit the zero lowerbound-real interest rates become negative as nominal rates cannot be zero. In suchsituations it can be challenging and prolonging to stimulate the economy especially atthe 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 interestrate is zero and monetary policy is powerless (Romer, 2006). Critics argue that ITmatters less for inflation and its stability and thus it is merely a “conservative windowdressing”. They argue it is the central bank’s greater emphasis and aversion towardsinflation that is important and not IT per se. The credibility effects can lead to better tradeoffs because policy changes can affectexpected as well as actual inflation – a central bank which agents believe will beinflation hawk in the future will not have to contract output by as much today to achievea given disinflation – Phillips curve becomes steeper. Furthermore, a credibledisinflation policy widely believed by agents or general public will cause inflationexpectation to decline rapidly and thereby shift down the Phillips curve without a largeoutput loss and hence resulting in smaller output losses and society having to pay lowersacrifice ratio. This is sometimes referred as ‘credibility bonus.’ It is commonly arguedthat enhanced communication and accountability of the central bank under IT shouldmake announced inflation objectives more credible and hence disinflations less costly.However there are problems with this result. If higher credibility leads to greaternominal wage – price rigidity for instance by perpetuating labour contracts, then thiscan offset direct effects of improved credibility. For instance when a credible monetaryregime produces low inflation environment, firms does not change their pricesfrequently and are less afraid to catch up if costs rise. And as the central banks becomemore inflation averse, labour unions may choose less wage indexation and perpetuatetheir wage contracts implying greater wage-price rigidity and hence flatter Phillipscurve (Clifton et al., 2001). Hutchison and Walsh (1998) mention that lower averageinflation by establishing credibility can increase nominal rigidity and worsen thetradeoff – Phillips curve becomes flatter – the net effect is ambiguous. In the following sections, the paper applies panel data analysis to test the abovetheoretical claims made by proponents and critics of IT. 5
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3. PREVIOUS STUDIESSince the introduction and adoption of IT in the 1990’s, there has been growing activeresearch on whether implementation of this new monetary regime has been beneficialin terms of macroeconomic performance. So far the empirical studies are mixed andinconclusive, thus lacking consensus among researchers regarding the effectiveness ofIT. 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 sectionaldifference-in-difference estimation, Ball and Sheridan (2005) find no evidence thatadoption of this regime leads to improvement in economic performance i.e. inflation,growth and volatilities. Using similar procedure Christensen and Hansen (2007) forOECD economies from 1970 to 2005 found countries that have switched either toexchange rate regime or IT experienced improvements in inflation, output andvolatilities but former regime lead to better performance. Mollick et al. (2011) for theperiod 1986 to 2004 using static panel data techniques finds that adoption of IT leads tohigher output per capita for both developing and industrial economies. However underdynamic 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 beeneffective in reducing inflation rates in the industrial countries. However revising thefindings, Willard (2006) finds no such evidence. Mishkin and Schmidt-Hebbel (2007)using panel and instrumental variable (IV) estimation procedure with time and countryfixed effects, suggest that IT has been favourable to macroeconomic performance forboth industrial and emerging economies. However despite these results they find noevidence that IT countries produced better monetary policy outcomes relative to non-ITcountries. Biondi and Toneto (2008) for 51 countries from 1995 to 2004 uses D-GMMand S-GMM including time effects and Feasible Generalized Least squares with time andrandom country effects. Biondi and Toneto (2008) find no benefits to output growthdue to IT adoption among developing economies however it was successful in reducinginflation rates. The findings are opposite for developed economies but smaller inmagnitude. According to Mishkin (2004) institutional differences make inflationtargeting much more difficult operate in emerging economies than in developed 6
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economies. However others argue practicing IT leads to better macroeconomicoutcomes in developing economies (Bernanke et al., 1999; Svensson, 1997). Goncalvesand Salles (2008) using the methodology of difference-in-difference for the case ofemerging 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 to2006 using S-GMM and other dynamic panel techniques using both common time andcountry fixed effects for the case of emerging economies finds no empirical evidencethat IT matters in terms of behaviour of inflation, output growth, volatilities and foundthat IT did not lead to favourable output inflation tradeoffs. Using different methodologies, Lin and Ye (2007) using propensity score matchingmethods for seven industrial countries from 1985 to 1999 find no evidence that IT hadimpacts on inflation and on its volatility. Walsh (2009) using a similar methodologyfinds no evidence that IT was effective in reducing inflation and economic volatilityamong developed economies however results are more favourable for developingeconomies. Nevertheless Vega and Winkelried (2005) also using propensity scorematching methods for a sample of developed and emerging economies find robustevidence that IT has helped reduce inflation and its volatility. Peturrson (2004) usingSeemingly Unrelated Regression finds that inflation has fallen after IT adoptionhowever it is statistically insignificant when lagged inflation is used as an additionalcontrol but remain significant for some countries. Affect of IT on output growth issignificant or borderline significant but find that output and inflation volatility hadfallen after the adoption of this regime. Goncalves and Carvalho (2007) for OECDeconomies using Heckman’s procedure find that IT countries suffered smaller outputloses during disinflation. However revising their findings, Brito (2009) again for OECDeconomies using panel GMM techniques finds no such evidence of a favourable tradeoffbetween inflation and output. As seen from above, results vary according to methodologies and data sets. Howeverpanel data has the advantage that it leads to more observations than cross sectionaldata. Also by exploiting the time and country dimensions, it can isolate improvementsdue to IT monetary regime from other sources that might be overlapping in a crosssectional framework. By introducing country fixed effects panel data can address theissue of omitted variable bias inherent in above studies for example Ball and Sheridan 7
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(2005) and lead to improvement on inference on the causal impact of IT onmacroeconomic indicators of interest. According to Biondi and Toneto (2008) paneldata is more informative, provides more efficient estimates of parameters, allowing thestudy of dynamics and control for unobserved heterogeneity of individual countries.Most of the findings above fail to take into account the short run relationship betweeninflation variability and real economic activity as implied by the Accelerationist Phillipscurve because as Mankiw (2001) mentions that inflation-output tradeoff is inexorable.Therefore not acknowledging this tradeoff casts doubt on some of the findingsregarding IT as an effective monetary policy strategy for economic performance. AsBrito and Bystedt (2010) mentions, inflation reduction in isolation simply implies thatIT central banks are more risk averse towards inflation than non-IT counterparts. As faras the difference-in-difference estimation procedure is concerned, Bertrand et al.(2004) mention that it may erroneously produce causal relationship between ITadoption and macroeconomic indicator and it also ignores vital time series informationin the data. This approach does not take into account the endogenous choice of ITadopted by countries with different observable and unobservable characteristics (Uhlig,2004). Although the propensity score methods deal with self selection problems, itscross sectional nature does not control for time effects, unobserved countryheterogeneity and persistence. Given that it ignores past information the IV withingroup estimation procedure of Mishkin and Schmidt-Hebbel (2007) is not efficient. Therandom effect analysis used by Biondi and Toneto (2008) is not suitable as individualspecific effects can be correlated with the explanatory variables and do not consider theimpact 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 betterinstrumented to capture the effects of high persistent variables (Arellano and Bover,1995; Blundell and Bond 1998). Brito and Bystedt (2010) uses two-step S-GMMestimator but only for the case of emerging economies. As mentioned aboveinstitutional differences and weaknesses, preconditions (for instance technicalcapability of the central bank, absence of fiscal dominance and sound financial markets)and relatively late adoption imply that IT will have less favourable and desiredmacroeconomic impacts in emerging economies. 8
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The aim of this paper is to re-assess the impact of IT on macroeconomicperformance by taking into account some of the shortcomings and discrepancy in theabove findings. Hence the aim and the contribution of this paper to the existing studiesis 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-GMMdue to Arellano and Bover (1995) and Blundell and Bond (1998), also conductingextensive robustness analysis.4. DATAThe data consists of an unbalanced panel of 39 developed economies 3 from the period1980 to 2009. Table 4.1 lists the economies included in the data.Table 4.1: Countries included in the sample4Inflation Targeting Year of adoption Inflation target rate Non Inflationcountries Targeting countriesAustralia 1993 2-3% Austria NetherlandsCanada 1991 1-3% Belgium PortugalChile 1991 2-4% Denmark SingaporeCzech Republic 1997 3%(±1%) Cyprus SlovakiaHungary 2001 3% (±1%) Estonia SloveniaIceland 2001 2.5%(±1.5%) Finland SpainIsrael 1992 1-3% France SwitzerlandSouth Korea 1998 3%(±1%) Germany TaiwanMexico 1999 3%(±1%) Greece USANew Zealand 1990 1-3% Hong Kong SARNorway 2001 2.5%(±1%) IrelandPoland 1998 2.5%(±1%) ItalySweden 1993 2%(±1%) JapanTurkey 2006 6.5%(±2%) LuxembourgUK 1992 2%(±1%) MaltaThe data consists of 15 economies that are IT and 24 that are non-IT. The data for thecountries inflation and real GDP growth rates were taken from IMF’s World Economic3 According 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). 9
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outlook database and World Bank’s World Development Indicators. The GDP series forEstonia, Slovakia and Slovenia starts from 1981, 1985 and 1991 respectively whereasfor 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 table4.1 ex-post experienced lower average inflation rates and inflation volatility asmeasured by standard deviation (SD) of inflation rates. Inflation rate is measured aspercentage 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-IT Mean SD Mean SD Mean SDAustralia 4.69 3.21 7.36 3.02 2.71 1.31Canada 3.61 2.96 6.35 3.12 1.82 0.74Chile 12.21 9.56 21.79 7.32 5.82 4.05Czech Republic 6.35 10.59 8.3 13.67 3.39 2.83Hungary 12.42 8.59 15.29 8.75 5.29 1.55Iceland 16.56 20.44 20.94 23.09 6.3 3.89Israel 43.01 83.55 99.49 112.12 4.96 4.28Mexico 31.56 35 46.21 37.47 5.22 1.7New Zealand 5.55 5.38 11.87 4.81 2.2 1.01Norway 4.29 3.41 5.28 3.6 1.84 1.07Poland 49.4 112.28 79.33 138.3 3.85 2.83South Korea 5.75 5.72 7.39 6.8 2.9 1.04Sweden 4.07 3.62 7.12 3.61 1.67 0.73Turkey 50.51 29.64 56.93 26.4 8.48 2.11UK 4.05 3.52 7.03 3.91 1.93 0.69IT15*† 16.94 22.5 24.55 26.4 3.56 1.98Note:*The average of statistics above†Excludes Turkey since it adopted in 2006 which is late compared to other IT countriesThe average inflation rates for IT countries fell from 24.55% in the pre targeting periodto 3.56% in end of post targeting period, an average by 20.99%. The volatility ofinflation 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 ITcountries and an important reason why central banks seem happy with their choice. 10
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Table 4.3 reports inflation statistics for non-IT countries also for the periods prior andafter 1990.Table 4.3: Inflation Statistics for Non-Inflation Targeting countries Entire Sample Pre-1990 Post-1990 Mean SD Mean SD Mean SDAustria 2.6 1.61 3.8 2.06 1.96 0.9Belgium 3.02 2.26 4.9 2.91 2 0.95Cyprus 4.06 2.74 5.77 3.74 3.14 1.59Denmark 3.51 2.85 6.33 3.46 2.04 0.59Estonia 77.57 238.32 80.74 244.4Finland 3.74 3.22 7.28 3.04 1.82 1.06France 3.71 3.64 7.34 4.38 1.81 0.78Germany 2.32 1.64 2.9 2.2 1.99 1.26Greece 11.45 8.28 19.5 4.07 6.41 5.18Hong Kong SAR 4.71 4.83 7.43 2.86 2.99 4.96Ireland 4.87 5.14 9.26 6.96 2.63 1.47Italy 5.95 5.43 11.43 6.3 3.06 1.44Japan 1.16 1.92 2.53 2.29 0.34 1.16Luxembourg 3.46 3.19 5.78 4.68 2.22 0.89Malta 2.64 2.29 2.27 3.82 2.8 1.01Netherlands 2.48 1.76 2.84 2.8 2.29 0.99Portugal 8.35 7.9 16.67 7.86 3.7 2.7Singapore 2.07 2.3 2.77 3.27 1.62 1.6Slovakia 7.66 5.21 7.66 5.21Slovenia 19.72 47.4 19.72 47.4Spain 5.85 4.09 10.25 3.94 3.49 1.6Switzerland 2.19 1.88 3.27 1.78 1.45 1.51Taiwan 2.83 4.35 4.64 7.03 1.81 1.66USA 3.71 2.58 5.55 3.62 2.65 0.98Non-IT24* 7.9 15.2 6.79† 3.96† 2.49 1.63Note:*The average of statistics above† Excludes Estonia, Slovakia and Slovenia.As these countries did not adopt IT, there is natural breaking point into pre and postperiods and hence the choice of the year 1990 is arbitrary, but rather it serves toillustrate how the era of IT has largely been an era of low and stable inflation for both ITand non-IT countries as table 4.3 illustrates. From table 4.3 all non-IT countries alsoexperienced low and stable inflation except for Malta which only experienced fall in 11
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inflation volatility. Hence it is evident that these countries have less incentive to pursueIT as inflation rates were fairly low and stable. A simple difference-in-differencecomparison suggests some impact of IT as inflation fell from 28.41% to 8.96% betweenthe 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 explicittargeting will conflict with other macroeconomic goals i.e. real economy objectives andlead to greater output volatility as can be seen from the loss function (2.2). Intermediatemonetary economics especially in short run generally suggest a tradeoff betweeninflation and output stabilization. Hence in accordance with this, IT which puts moreweight on inflation stabilization should lead to greater output volatility. Tables 4.4 and4.5 illustrate the average growth rates and output volatilities measured by the standarddeviation 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 Mean SD Mean SD Mean SDAustralia 3.26 1.71 2.82 2.33 3.57 0.99Canada 2.53 2.22 2.78 2.44 2.63 1.88Chile 4.56 4.77 3.58 6.67 4.97 3.33Czech Republic 1.81 3.68 1.28 4.02 2.85 3.13Hungary 1.27 3.66 0.958 3.75 1.79 3.72Iceland 2.92 3.51 2.9 3.1 2.84 4.8Israel 4.21 2.51 3.85 1.88 4.3 2.89Mexico 2.55 3.78 2.91 4.04 1.75 3.51New Zealand 2.33 2.23 1.94 1.99 2.65 2.37Norway 2.75 1.79 3.19 1.75 1.68 1.62Poland 2.25 4.62 1.03 5.5 4 1.9South Korea 6.57 4.18 8.22 3.34 4.98 2.99Sweden 2.08 2.39 1.87 1.87 2.55 2.54Turkey 4.01 4.47 4.33 4.39 0.21 4.69UK 2.14 2.2 2.02 2.44 2.34 2.09IT15*† 3.02 3.18 2.81 3.23 3.06 2.7Note:*The average of statistics above†Excludes Turkey 12
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Table 4.5: Output growth Statistics for Non-Inflation Targeting countries Entire Sample Pre-1990 Post-1990 Mean SD Mean SD Mean SDAustria 2.04 1.59 1.82 1.16 2.05 1.77Belgium 2.02 1.63 2.16 1.6 1.89 1.72Cyprus 4.72 2.74 6.13 1.99 3.84 2.79Denmark 1.72 2.17 1.9 2.22 1.63 2.25Estonia 1.9 7.55 2.74 1.58 1.98 9.09Finland 2.43 3.3 3.55 1.3 1.94 3.94France 1.88 1.43 2.35 1.19 1.6 1.52Germany 1.7 1.97 1.87 1.48 1.37 2.03Greece 2 2.32 0.78 2.3 2.75 2.09Hong Kong SAR 5.07 4.13 7.44 4.25 3.88 3.7Ireland 4.29 4.04 2.4 1.76 5.1 4.62Italy 1.33 1.86 2.06 1.74 0.91 1.89Japan 2.16 2.67 4.4 1.46 0.81 2.23Luxembourg 4.43 3.13 4.94 3.46 4.11 3.09Malta 3.78 2.79 4.01 3.02 3.49 2.73Netherlands 2.16 1.95 1.81 1.94 2.24 2Portugal 2.7 2.59 3.69 2.84 1.9 2Singapore 6.76 4.05 7.81 4.15 6.04 4Slovakia 2.46 5.49 2.67 1.21 2.68 6.19Slovenia 2.44 4.59 2.44 4.59Spain 2.7 2.07 2.72 2.05 2.64 2.18Switzerland 1.73 1.76 2.38 1.89 1.29 1.59Taiwan 5.82 3.02 7.7 2.64 4.78 2.84USA 2.68 2.08 3.05 2.54 2.5 1.9Non-IT24* 2.96 2.96 3.49 2.16 2.67† 2.96†Note:*The average of statistics above† Excludes SloveniaAmong the IT countries, five countries (Canada, Mexico, Norway, South Korea, Icelandand 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 ofpost 1990 period. On the evidence presented in table 4.4, IT in general has not beenassociated 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 13
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be associated with good luck view of ‘Great Moderation’ period. Nevertheless, amongnon-IT countries except USA, Switzerland, Singapore, Portugal, Malta, Hong Kong andGreece experienced increased output volatility. Tables 4.3 to 4.5 suggest that both ITand 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 againsttime for both IT and non-IT economies. Figure 4.1: Average Inflation 70 Non Inflation Targeting 60 countries Inflation Targeting countries 50 40Rate 30 20 10 0 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 YearThe gap between average inflation rates and inflation volatility between IT and non-ITeconomies is more pronounced before early 1990’s. The gap between these twomeasures diminishes during the targeting periods i.e. 1990’s and onwards henceimplying monotonic convergence. This reinforces the finding that after the period 1990,there was a greater aversion among central banks among IT and non-IT economiestowards inflation. Pertaining to inflation, the results so far emphasize that inflationvolatility 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 14
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Figure 4.2: Inflation Volatility 250Standard Deviation of Inflation rate Non Inflation Targeting 200 countries Inflation Targeting 150 Countries 100 50 0 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 Yearnon-IT economies enjoyed periods of favourable growth in 1990’s where 11 countriesin the sample adopted IT however both groups faced slump in the early 2000 andtowards the end of the sample. These were the periods where developed economiessuffered recessionary effects due to external shocks. Importantly the average growthrates of IT countries were closely followed by the average growth rates of non-ITcountries thus suggesting growth behaviour was the same for these groups. Figure 4.4suggests that both group of countries faced a fall in average output volatilities duringthe inflation targeting periods i.e. year 1990 and onwards. However since early 1990’still the end of the sample output volatility for non-IT economies were confined within2% to 3%. Hence figure 4.4 suggests that both IT and non-IT countries faced favourabletradeoffs in terms of inflation and output volatility. Looking at the data in this way is informative and suggestive, however it is notconclusive. The above descriptive analyses do not constitute an evidence of causalrelationship between IT and better economic outcomes, is bivariate and it does notaccount for changes in other variables that may affect the macroeconomic indicator ofinterest. From the above information summarized, IT is associated with lowering ofinflation for all IT countries, but central banks also achieved lower inflation without anyexplicit targeting. During the 1990’s many countries experienced lower and stableinflation rates due to changes in the structural characteristics in labour markets. Ihrig 15
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and Marquez (2004) finds that among 19 industrialized countries persistent labourmarket slack was the main factor exerting downward pressure for inflation in additionto acceleration in productivity effects for USA. Labour market reforms helped to pushdown inflation dramatically in Ireland, Norway and New Zealand. Figure 4.3: Average output growth 6 4 2Rate 0 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 -2 Non Inflation Targeting countries -4 Inflation Targeting Countries -6 Year Figure 4.4: Output Volatility 7 Non Inflation TargetingStandard Deviation of output growth 6 countries Inflation Targeting 5 countries 4 3 2 1 0 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 Year 16
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Furthermore the decline in output volatility over the most of the course of last twodecades 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 onthe importance of IT on macroeconomic outcomes.5. METHODOLOGYFor estimating long and short run elasticities researchers often use a form of ageometric lag model called the partial adjustment model. The following partialadjustment model is utilized: (5.1)where is inflation rate, growth rate, inflation or output growth volatility. Thesubscript indexes country; is the time period. The termis included to capture persistence and mean reverting dynamics and as a consequencethere 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 t and 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 smallnumber of countries with high inflation. Another motivation to use this log transform isthat simple log transform to down weight very large readings, over weights readingsthat 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, insimulation studies, Judson and Owen (1999) found a bias equal to 20% of the coefficient 17
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of interest even when T = 30. Standard results for omitted variables indicate that atleast in large samples, the FE estimator and OLS are biased downwards and upwardsrespectively (Bond, 2002). Given the possibility of reverse causation on inflation (or other macroeconomicindicators) on IT, or a third omitted time variant factor causing both IT adoption andinflation reduction and that both OLS and FE yields biased and inconsistent estimatesprovides the motivation to use D-GMM estimation for the model (5.1) that controls forboth simultaneity and omitted variable bias. The D-GMM estimation strategy is due toHoltz-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 correlatedwith future disturbances), D-GMM approach consists of differencing (5.1) to expungethe country fixed effects and to apply the following moment conditions oninstruments : (5.2)where Using these moment conditions, Arellano and Bond(1991) proposes a two-step GMM estimation. In the first step the error terms areassumed to be homoskedastic and independent across countries and over time. In thesecond step, the residuals obtained in the first step are then used to construct aconsistent estimate of the variance-covariance matrix for the second-step estimation,therefore relaxing the assumptions of independence and homoskedasticity. Thus thetwo-step estimation is asymptotically more efficient than one-step even when theerrors are homoskedastic. To correct for the downward bias of two-step standarderrors, the Windmeijer’s (2005) finite sample correction procedure is used to the two-step estimator variance-covariance matrix. Hence this paper uses only two-stepestimation. While GMM approaches are more suited to micro data where N is large relative to T,it can cause problems in macro data where T is large relative to the number ofcountries, N, because the number of instruments, function of T, climbs towards thenumber of countries, N. As Roodman (2009) mentions this instrument proliferation 18
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problem can bias the results by over-fitting the instrumented variables. To deal withthis problem the data is summarized over many 3 year periods as in Islam (1995) andAcemoglu et al. (2008). Averaging the data over intervals means that results are lesslikely 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 forslow response of macroeconomic variables and isolating the IT treatment effects fromevents occurring in close proximity. This allows entering information contained in along time series into smaller time periods while holding down the number ofinstruments. As mentioned in Roodman (2009), to overcome instrument proliferationproblem and hence over fitting, the dimensionality of the matrix of instruments isreduced by collapsing its columns. Columns of the instrument matrix embodying themoment conditions in (5.2) for all t and s are collapsed into a single moment conditionas for all s, as in Calderon et al. (2002). A potential drawback of D-GMM is that it leads to low precision and finite samplebiases when the time series is a highly persistent process; lagged levels of variables arepoor instruments for first differences (Blundell and Bond, 1998; Bond et al., 2001).Since it is reasonable that inflation and IT dummy variable are persistent processestheir past values convey little information about future changes and hence provide poorinstruments for the transformed equation in differences. To increase efficiency analternative approach, the S-GMM, was suggested by Arellano and Bover (1995) andBlundell and Bond (1998). To increase efficiency, Blundell and Bond (1998) suggestalso using the moment conditions5: (5.3)where the fixed effects are expunged from the instruments using orthogonal deviationsas used by Arellano and Bover (1995) and mentioned in Roodman (2006), and usingthese moment conditions with (5.2) in S-GMM approach. Hence S-GMM approach5 Only the most recent lagged differences are used as instruments. Using other lagged differences ininstruments results in redundant moment conditions given the moment conditions exploited in (5.2) (seeArellano 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). 19
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augments the D-GMM approach by using lagged values as instruments for regression indifferences with lagged differences as instruments for regression in levels. That is S-GMM estimation combines in a system the regression in differences with regression inlevels. The above moment conditions are valid if changes in any instrument areuncorrelated with the fixed effect i.e. for all z and 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 forthis 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 assumption 6. Looselyspeaking countries in the sample are in steady state in this sense that deviations fromlong term values after controlling for covariates are not systematically related to fixedeffects. 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 mentionedearlier. 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 andefficiency gains for small T and persistent series. The results are also corroborated byHahn (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 higherefficiency. This is especially important for macro data or in empirical growth literaturewhen N, the number of countries is small and size of T is moderate. To test the validity and consistency of the GMM, specification tests are employed asmentioned in Arellano and Bond (1991). The consistency of the GMM estimators6 See Blundell and Bond (1998) for details on this assumption. 20
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presented above relies that there is no second-order serial correlation in the firstdifferenced disturbances, . But by construction might be first-order seriallycorrelated even if is not. The additional moment conditions are over identifyingrestrictions and to test their validity, tests of over indentifying restrictions are used. Totest the validity of additional restrictions for D-GMM and S-GMM, Hansen’s (1982) J testis used and to test the additional moment conditions that are used for regression inlevels in S-GMM, difference in Hansen C test is used. This tests statistic tests for thevalidity of subsets of instruments used for equation in levels whereas the Hansen J test,tests the overall validity of instruments. To overcome the weaknesses of tests of overidentifying restrictions due to instrument proliferation the size of the matrix ofinstruments is collapsed as mention above. Sargan and difference in Sargan tests are notvulnerable to instrument proliferation but they require homoskedastic errors forconsistency which is rarely assumed (Roodman, 2009). If Hansen J and C test statisticrejects 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 theabove tests fail to reject the null, then this lends support to the model, validity ofmoment conditions and its specification.6. RESULTS6.1. PRELIMINARY RESULTSTables 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 t and 0 otherwise.To prevent bias in the favour of the IT dummy, high inflation dummy is partiallycontrolled using the dummy which is equal to 1 if average inflation is greaterthan 0.20 per year (in natural logarithm) in period t and 0 otherwise. As in the spirit ofBall 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 21
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IT on real economy as Bruno and Easterly (1998) and Barro (1996) have recognizeddifferences 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 ERegressors: (1) (2) (3) (4) (5) (6)6.1.A- Inflation equationInflation 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 0.06AR(2) test 0.13 0.18 0.12 0.13Hansen J test 0.51` 0.18 0.09 0.07Difference-in-Hansen 0.59 0.81Observations 340 340 301 301 340 340Instrument columns 29 28 33 32R-squared 0.59 0.446.1.B Output growth equationInflation 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 0.00AR(2) test 0.42 0.48 0.41 0.41Hansen J test 0.20 0.16 0.28 0.23Difference-in-Hansen 0.83 0.81Observations 343 343 304 304 343 343Instrument columns 29 28 33 32R-squared 0.38 0.38p-values in parentheses. AR(1), AR(2), Hansen J tests, and difference-in-Hansen report the respective p-values.(1)-(2) uses robust standard errors clustered by country.(3)-(6) uses Windmeijers (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). 22
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Column (1) of tables 6.1 and 6.2 presents pooled OLS results with time effects wherestandard errors are clustered by country. The time effect captures the worldwide trendevents and productivity changes common to all countries. Results show that IT has beenineffective 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 onoutput 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 OLSpresents that IT has been unsuccessful in reducing inflation and inflation volatility.Rather the positive sign indicates that it produced adverse effects on these twovariables 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 whereestimation indicates that IT has favourable impact on inflation with adverse costs interms of output growth (tables 6.1.A and 6.1.B) and was ineffective in stabilization ofinflation 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 WGsuffers from dynamic panel bias (Nickell, 1981) where the direction of the bias for OLSand WG is upwards and downwards respectively. Thus if there is a candidate consistentestimator, 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 and6.2 fixes the dynamic panel bias and takes into account the undisputable endogeneityof . For t≥3 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 ofinstruments 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 oflower output growth (tables 6.1.A and 6.1.B). There is no indication IT has beensuccessful in lowering macroeconomic volatilities (tables 6.2.A and 6.2.B). Howevernone of the results are significant. The GMM specification tests 7 also do not indicate aproblem of serial correlation of residuals using the AR(1) and AR(2) test statistics intables 6.1 and 6.2. As mentioned earlier that consistency of the GMM estimates crucially7 For details of the specification tests see Arellano and Bond (1991), Hayashi (2000) and Roodman (2006) 23
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depends on i.e. no second-order serial correlation for thedisturbances in the firstTable 6.2: Estimates of Inflation targeting effects on macroeconomic volatility (1980-2009)Estimator: TE-OLS WG D-GMM P D-GMM E S-GMM P S-GMM ERegressors: (1) (2) (3) (4) (5) (6)6.2.A- Inflation volatility equationInflation 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)High inflation dummy 22.2 30.5 28.6 28.9 48.8 49.5 (0.02) (0.02) (0.08) (0.08) (0.06) (0.05)AR(1) test 0.199 0.20 0.22 0.23AR(2) test 0.24 0.25 0.16 0.17Hansen J test 0.02 0.02 0.02 0.01Difference-in-Hansen 0.00 0.00Observations 340 340 301 301 340 340Instrument columns 29 28 33 32R-squared 0.3 0.256.2.B Output growth volatility equationInflation 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)High inflation dummy 1.92 1.89 2.40 2.4 1.46 1.51 (0.00) (0.00) (0.13) (0.15) (0.02) (0.02)AR(1) test 0.00 0.00 0.00 0.00AR(2) test 0.87 0.88 0.96 0.95Hansen J test 0.72 0.70 0.29 0.24Difference-in-Hansen 0.14 0.12Observations 342 342 303 303 342 342Instrument columns 29 28 33 32R-squared 0.38 0.38p-values in parentheses. AR(1), AR(2), Hansen J tests, and difference-in-Hansen report the respective p-values.(1)-(2) uses robust standard errors clustered bycountry.(3)-(6) uses Windmeijers (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). 24
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differenced equation which tests for serial correlation for disturbances in levels(Roodman, 2006). The Hansen J test which tests the overall validity of the instrumentsis also not rejected in tables 6.1.A, 6.1.B and 6.2.B. However D-GMM results aredisappointing. 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 orclosely following a random walk process and hence D-GMM performs poorly (leads tofinite sample bias) in terms of precision. Therefore past levels of variables provide weakinstruments (becomes less informative) for equation in differences for D-GMM. Outputis a persistent process and as mentioned earlier so are inflation and IT dummyvariables. To increase efficiency a more appropriate approach of S-GMM due to Arellano andBover (1995) and Blundell and Bond (1998) is used which exploits additional momentrestrictions as mentioned above. Columns (5) and (6) produce the two-step S-GMMestimates. For t≥3 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 endogenousto address possible reverse causality from inflation and/or output growth to IT.Alternatively to take into account a third country specific time varying factor thatsimultaneously determines both the macroeconomic performance and the monetarypolicy. Then for t≥3, 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 andweakly 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 werenot successful in reducing the inflation rates relative non-IT economies. In column (5) in6.1.A IT imposes a negative impact of 1.77% per year on inflation rate. 25
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Columns (5) and (6) in contrast to D-GMM estimates in (3) and (4) of table 6.1.B alsoconfirms the weak instrument problem (output is persistent process as indicated bylagged 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 inferringthe 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 bank’s have been more flexible with IT policy andplaced relatively greater weight towards growth. S-GMM estimates indicate that IT was not effective at stabilizing inflation and outputbut the results are largely insignificant (tables 6.2.A and 6.2.B). Again as for outputgrowth in table 6.1.B there is weak instrument problem for D-GMM estimates in table6.2 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 weakinstrument problem is to consider the differences in coefficient estimates of OLS, WGand unbiased GMM estimator. In column (1) table 6.2.B for example, OLS provides anestimate of -0.08% per year impact of IT on output volatility and in column (2) WGprovides 0.21% whereas an unbiased GMM estimate in column (6) of table 6.2.B yields0.05%. This technique along with comparing S-GMM estimates relative to D-GMMestimates and/or computing , as done for output growth in the precedingparagraph revels the persistence of the series and the nature of the weak instrumentproblem. The S-GMM estimate in column (6) where IT is treated as endogenous in tables6.1.A and 6.2.A reveals the simultaneity existent between inflation, inflation volatilityand IT regime as indicated by large changes in magnitude and direction of thecoefficient estimates, thus indicating IT is influenced by the average inflation andinflation 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 directionthus suggesting that main cause of endogeneity bias is reverse causality from inflationand its volatility to IT. The specification tests do not reject the S-GMM estimates for output and outputvolatility (tables 6.1.B and 6.2.B). Another evidence of consistency is that both laggedoutput and output volatility GMM estimates are between the OLS and WG estimates. On 26
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a worrying note Hansen J test is weakly insignificant for the average inflation equationfor the S-GMM estimates in columns (5) and (6) of table 6.1.A. However the test statisticrejects the validity of the overall instruments for the inflation volatility equation in 6.2.Afor both D-GMM and S-GMM specifications. Furthermore the difference in Hansen C test,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 theefficiency gain from S-GMM is not free; we need extra assumptions and the violationwhich leads to bias. The weak exogeneity of some of instruments as indicated by HansenJ test in table 6.1.A for inflation raises some concerns and doubt regarding the S-GMMestimates. 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 theendogeneity 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 hadsignificant or marginal significant effect on output growth where the impact per yearlying in 0.57% to 0.71% range. If the lagged coefficient α which controls for meanreversion or regression to mean is significant and between 0 and 1 and IT dummycoefficient β is insignificant then it implies that countries that had higher inflation saw agreater decline in inflation than already low inflation countries. Similar analogy alsoapplies to output and volatilities. In contrast to simple regression to mean evidencefound in Ball in Sheridan (2005) for inflation, table 6.1.A for inflation does not indicatethis is the case. Thus the significant or marginal significant IT impact on output growthis 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 affectsinflation and promotes greater volatilities in the economy hence suggesting that in highinflation periods macroeconomic indicators have different long run means. Also asexpected high inflation has a negative impact on growth confirming the findings thatcountries going through high inflation grew less (Bruno and Easterly, 1998) but resultsare not significant.8 Uhlig (2004) mentions that choice of IT has been an endogenous one by the countries that has adopted it. 27
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6.2. 1985-2002To examine the sensitivity of the above results to different sample period, the period1985-2002 is chosen9. In columns (5) and (6) of table 6.3.A the difference in Hansen Ctest rejects the validity of additional moment conditions for S-GMM estimates for theinflation equation. The test statistic weakly rejects (at 10%) the extra instruments inlevels for the S-GMM when IT is treated as predetermined variable but when IT isendogenous 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 Blundelland 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. evenafter taking into account lagged inflation. There may be indication of endogeneity as thecoefficient estimate in (4) is more negative. The D-GMM estimates in columns (3) and (4) of table 6.3.B indicate that IT has beenadverse for output growth for the 1985-2002 period imposing a significant negativecost of -7.97% to -8.42% per year impact. Nevertheless as in table 6.1.B, the S-GMMestimates reveal weak instrument problem of D-GMM results in table 6.3.B for outputequation. S-GMM estimates indicate that IT did not have any significant impact onoutput growth for IT economies. The S-GMM results in columns (5) and (6) of table6.3.B also reveals the importance of taking into account the endogeneity of IT as themagnitude of IT per year impact on output growth estimate changes from 0.24% to0.09% in table 6.3.B for the output growth equation. Hence the S-GMM estimates incolumns (5) and (6) are preferred results and they are fairly robust to sample periods ina sense that IT is not found to have any adverse impact of output growth andfurthermore the specification tests do not reject the validity of the instruments used andconsistency. But now there is some evidence of simple regression to mean effect. Laggedoutput growth estimates in table 6.3.B also lie between OLS and WG estimates – furtherevidence of consistency.9 This period was also used by Wu (2004) and Willard (2006). 28
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Table 6.3: Estimates of Inflation targeting effects on inflation and output growth (1985-2002)Estimator: TE-OLS WG D-GMM P D-GMM E S-GMM P S-GMM ERegressors: (1) (2) (3) (4) (5) (6)6.3.A Inflation equationInflation 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 0.53AR(2) test 0.18 0.70 0.68Hansen J test 0.92 0.86 0.33 0.15Difference-in-Hansen 0.07 0.02Observations 190 190 151 151 190 190Instrument columns 15 14 19 18R-squared 0.52 0.536.3.B Output growth equationInflation 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 0.05AR(2) test 0.64 0.49 0.71 0.73Hansen J test 0.78 0.84 0.61 0.46Difference-in-Hansen 0.15 0.10Observations 192 192 153 153 192 192Instrument columns 15 14 19 18R-squared 0.31 0.28p-values in parentheses. AR(1), AR(2), Hansen J tests, and difference-in-Hansen report the respective p-values.(1)-(2) uses robust standard errors clustered by country.(3)-(6) uses Windmeijers (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). The endogeneity bias as well as the weak instruments problem is also apparent fromestimates 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 29
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6.4.B indicate that IT was favourable in reducing macroeconomic volatility but did nothave any significant effects.Table 6.4: Estimates of Inflation targeting effects on macroeconomic volatility (1985-2002)Estimator: TE-OLS WG D-GMM P D-GMM E S-GMM P S-GMM ERegressors: (1) (2) (3) (4) (5) (6)6.4.A Inflation volatility equationInflation 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)High inflation dummy 20.50 25.30 8.94 9.84 1.48 0.83 (0.05) (0.03) (0.33) (0.30) (0.63) (0.77)AR(1) test 0.80 0.65 0.46 0.42AR(2) test 0.62 0.59 0.28 0.18Hansen J test 0.21 0.49 0.18 0.12Difference-in-Hansen 0.24 0.02Observations 189 189 150 150 189 189Instrument columns 15 14 19 18R-squared 0.27 0.236.4.B Output growth volatility equationInflation 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)High inflation dummy 2.94 2.82 4.87 6.08 4.51 4.98 (0.00) (0.00) (0.16) (0.02) (0.07) (0.01)AR(1) test 0.01 0.01 0.01 0.01AR(2) test 0.69 0.76 0.85 0.72Hansen J test 0.21 0.48 0.28 0.31Difference-in-Hansen 0.38 0.20Observations 192 192 153 153 192 192Instrument columns 15 14 19 18R-squared 0.28 0.31p-values in parentheses. AR(1), AR(2), Hansen J tests, and difference-in-Hansen report the respective p-values.(1)-(2) uses robust standard errors clustered bycountry.(3)-(6) uses Windmeijers (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). 30
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The difference in Hansen C tests however rejects the additional assumptions made oradditional moment restrictions used in S-GMM at 5% level for the inflation volatilityequation in column (6) of table 6.4.A where IT is treated as endogenous variable. In thisevent the D-GMM estimates are again consistent and are accepted by specification testshowever they suggest that IT was ineffective in reducing macroeconomic volatilities butresults are once again not significant at any reasonable significance level. Thespecification tests do not reject the moment conditions for output growth volatilityequation in 6.4.B and no second-order serial correlation is found. Hence for outputvolatility in table 6.4.B the additional instruments seem to be valid and highlyinformative. Furthermore lagged output growth volatility coefficients lies in betweenOLS and WG estimates – further evidence of consistency. Once again results are robusti.e. IT is not found to have any significant adverse impact on inflation volatility as welloutput growth volatility across the two sample periods.6.3. ROBUSTNESS ANALYSISSo far, the empirical evidence showed that IT didn’t have any significant or adverseeffects 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-GMMestimation which is expected to be more efficient however specification tests areagainst the S-GMM results as found above (table 6.3.A). IT was shown to have positivesignificant impact on output growth but for the period 1985-2002 it due to meanreversion. An important question is that are these results robust? To further test the sensitivity of the results, reduced sets of instruments are usedwhere only until lag 3 instruments are used. As Roodman (2009) mentions it isimportant to always check for robustness of the analysis using reduced instruments.Different IT adoption dates are also materialized 10 using reduced instrument sets tofurther check for sensitivity. For Chile, Czech Republic, Israel and Mexico IT adoptiondates according to Batini and Laxton (2007) are used. For Australia, Canada, Finland,10 When using full set of instruments for different IT dates, conclusions regarding IT effects do not changesignificantly but some specification tests are not supportive regarding the validity of the models hence reducedset of instruments are used for different IT adoption dates analysis. 31
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New Zealand and UK adoption dates for constant IT11 i.e. meaning unchanging target ortarget range. The results are presented for S-GMM only12 as Hayakawa (2005) findsanalytically and experimentally that despite using more instruments S-GMM is moreefficient than D-GMM. In columns (1)-(4) of table 6.5.A the S-GMM estimates of the effects of IT on inflationis 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 withdifferent IT adoption dates in (3) and (4). Consistent with above findings for the sampleperiod 1985-2002, in columns (5) and (6) of table 6.5.A IT produces positive butinsignificant impact in reducing inflation when used with reduced instruments. Hencethere is a paradox i.e. IT was largely ineffective in reducing inflation, but there is someevidence, albeit weak that it had a positive impact on inflation. This may indicatemultiple hypotheses. In 1980-2009, IT regime has been increasingly flexible anddiscretionary (pursuing expansionary polices) compared to 1985-2002 period givingmore weight to output growth. A closer examination of figure 3.1 in section 3 revealsthat 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 anchoringpublic’s expectations of inflation and to mimic policy under commitment thus failing toestablish credibility (see section 2 on theory) therefore unable to produce lowerinflation rates. A third view is that central banks during the last few years have purseddiscretionary monetary or fiscal policies to prevent the spread of deflationaryexpectations that may have been present in the period 1985-2002. But overall at facevalue and generally, the IT results for average inflation from tables 6.1.A, 6.3.A and 6.5.Aindicates that it has largely been unsuccessful in reducing in inflation and this abideswith the results found in Ball and Sheridan (2005) and Willard (2006) but results arefairly robust for its adverse effects on inflation. However there is some evidence that ITmatters for inflation according to D-GMM estimates in columns (3) and (4) table 6.3.Abut as mentioned earlier they may be severely biased due to weak instruments problem.Either central banks were not able anchor inflation expectations and thus establishcredibility or they were too flexible. This clearly implies two things. Firstly, given the11 See 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 indicatingthe weak instrument problem. 32
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