1 Does Inflation Targeting Make a Difference? Klaus Schmidt-Hebbel Central Bank of Chile (jointly with Frederic S. Mishkin, Columbia University) 8 th Annual Meeting of the Central Bank of Brazil Rio de Janeiro, 11 August 2006

2 FOUR QUESTIONS Open questions on comparative performance of ITers and NITers: 1. Is inflation lower under IT (Ball and Sheridan 2005), controlling for different country samples, dynamics, and IT regime endogeneity? (Mishkin and Schmidt-Hebbel 2002) 2. Do monetary policy and macro variables respond differently to shocks under IT? 3. Are inflation and output volatility lower under IT and is monetary policy more efficient in IT countries? 4. Are ITers more accurate in hitting inflation targets than NITers in achieving stable inflation?

3 OUR EMPIRICAL APPROACH 1. Compare 21 industrial and EM ITers before and after adoption of IT to stringent control group of 13 successful industrial NITers (U.S., Japan, and 11 European countries) 2. Distinguish 2 IT regimes: Converging and Stationary ITers 3. Test for differences in group behavior of: (i) pre-IT vs. post-IT for ITers, (ii) ITers vs. NITers, (ii) converging and stationary ITers, and (iv) industrial and emerging-market economies, using panel estimations, panel VARs, and impulse responses 4. Use high-frequency quarterly data for

4 OUTLINE OF PAPER 2. Descriptive Inflation and Output Statistics 3. Comparative Inflation Performance 4. Inflation and Policy Response to Shocks 5. Inflation Volatility, Output Volatility, and Monetary Policy Efficiency 6. Inflation Accuracy 7. Concluding Remarks

5 2. Descriptive Inflation and Output Statistics Adoption of IT somewhat arbitrary: - we assume IT adopted even if partial But much easier and non-controversial to date converging IT (declining inflation target) and stationary IT (constant target) Tables 2.1 – 2.3 and Figs 2.1 – 2.2 in the paper summarize descriptive statistics on inflation levels, volatility, and persistence, and volatility and persistence of GDP growth and the GDP output gap (for some 2,000 quarterly observations).

6 2. Descriptive Inflation and Output Statistics Looks like IT helps reduce inflation toward NITers’ levels …. …. but needs more systematic tests.

7 3. Comparative Inflation Performance We start by replicating difference-in-difference OLS estimation of previous studies, applied to cross-sec. sample: (3.4)  i post –  i pre =  1 +  2 D i –  3  i pre +  i, with results for the long-term difference of inflation in ITers largely driven by choice of control groups:

8 3. Comparative Inflation Performance BUT: results may be affected by lack of time dimension and endogeneity of IT regime to inflation. Hence we run pooled OLS, panel IV, and pooled IV regressions (instrumenting IV dummy) and with time and country fixed effects on our world quarterly data sample for the following specification: (3.3)  it =  D it + (1– )  it-1 + α i +  t +  it with the following results for the LT inflation difference: (note: blank cells correspond to  coefficient estimates that are not significantly different from zero)

9 3. Comparative Inflation Performance LT Inflation-level Difference CG1 (NITers and pre-ITers) CG2 (NITers) CG3 (pre- ITers) All ITers (OLS)-1.9%zero-5.0% All ITers (IV)-4.8%zero-5.0% Industrial (IV)zero-1.1%zero Emerging (IV)-7.5%zero-6.4% Stationary (IV)-2.1%zero Converging (IV)-8.0%zero-8.2%

10 4. Inflation and Policy Response to Shocks We test for differences in dynamic response of IT and NIT country groups to: – inflation response to oil price and exchange rate shocks – domestic interest rates to international rates (MP independence) Method: –GMM system estimation of fixed-effects panel VARs for 6 variables and 5 country groups (ex.: Love and Zicchino 2002) –impulse responses identified using Choleski decomposition of variance-covariance matrix of residuals –confidence intervals constructed by bootstrapping method –confidence intervals for impulse-response differences between country group constructed by bootstrapping, too.

11 4. Inflation and Policy Response to Shocks Results on Inflation and Oil-Price Shocks –Inflation responds less to oil-price shock after adoption of IT (both all ITers and stat. ITers) – but differences are mostly not significant – Inflation responds slightly more to oil-price shocks in stat. ITers than in NITers during the first 2 quarters but significantly less in quarter 6. –In emerging-market ITers and in industrial ITers (separately), inflation responds less to oil-price shocks than in NITers – particularly in stationary EME ITers. Hence inflation response to oil shocks has declined with IT, even beyond the response in NITers.

12 4. Response of Inflation to a Shock in Oil Prices Before start of IT After start of ITDifference ITers Before start of IT After start of ST ITDifference Before 1997After 1997Difference NITers ITers before start of ITNIters after 1997Difference ITers vs. NITers ITers before start of ST ITNIters after 1997Difference

13 4. Inflation and Policy Response to Shocks Results on Inflation and Exchange-Rate Shocks (passthrough) Inflation responds significantly less to ER shocks after IT adoption in stationary ITers during first 3 quarters While inflation responds significantly to ER shocks in ITers (both all ITers and stationary ITers), the passthrough is zero in NITers. The latter result is driven by EME ITers, where the passthrough is still positive after IT (even after stationary IT), as opposed to IC ITers, where it is zero, like among NITers. Hence inflation response to ER shocks has declined with IT in EMEs. This is a result of the decline of passthrough among EME ITers. Among IC ITers, like among NITers, exchange-rate shocks do affect inflation significantly.

14 4. Response of Inflation to a a Shock in the Exchange Rate Before start of IT After start of ITDifference ITers Before start of IT After start of ST ITDifference Before 1997After 1997Difference NITers ITers before start of ITNIters after 1997Difference ITers vs. NITers ITers before start of ST ITNIters after 1997Difference

15 4. Inflation and Policy Response to Shocks Results on Domestic Interest Rates and International (U.S.) Interest Rate Shocks (Monetary Independence) Domestic rates (i) respond signif. less to international rate (i*) shocks after IT adoption in ITers (both all ITers and stationary ITers) i responds signif. more to i* in all ITers than in NITers - but not signif. more in stationary ITers However, the latter result is driven by EME ITers: they exhibit, after starting IT, still a larger response of i to i* IC ITers show the same (small) response of i to i* than that observed among NITers. Hence the response of i to i* has declined with IT (more monetary independence), particularly among EME ITers, but the latter still exhibit less monetary independ. than NITers do.

16 4. Response of the Domestic Interest Rate to a Shock in the International Interest Rate Before start of IT After start of ITDifference ITers Before start of IT After start of ST ITDifference Before 1997After 1997Difference NITers ITers before start of ITNIters after 1997 Difference ITers vs. NITers ITers before start of ST ITNIters after 1997Difference

17 5. Inflation Volatility, Output Volatility, and MP Efficiency We follow Cecchetti, Flores-Lagunes, and Krause (2004) in estimating and comparing improvements in MP efficiency and macro outcomes (inflation and output volatility). But instead of doing it for indiv. countries (as Cecchetti et al.), we apply their methodology to IT and NIT country groups. Method: Estimate aggregate demand and supply for country groups Solve optimal control problem of CB for optimal interest rate Compute inflation-output volatility efficiency frontier, varying CB inflation weight

18 5. Inflation Volatility, Output Volatility, and MP Efficiency Decompose observed differences in macro performance (inflation and output volatility) into changes in supply shock variability (changes in efficiency frontier) and changes in MP efficiency (distance from efficiency frontier), assuming =0.8 Results: Macro performance and monetary policy efficiency have increased by large amounts after IT adoption, both in all ITers and in stationary ITers – but gains are larger under stationary IT But MP efficiency is still higher among our control group of NITers than in IT countries.

19 5. Inflation Volatility, Output Volatility, and MP Efficiency Inflation variability

20 5. Inflation Volatility, Output Volatility, and MP Efficiency Inflation variability

21 5. Inflation Volatility, Output Volatility, and MP Efficiency Inflation variability

22 5. Inflation Volatility, Output Volatility, and MP Efficiency Other results (in the paper): NITers have also increased their macro performance massively since 1998 but their MP efficiency has declined somewhat since 1998 The differences between ITers and NITers is largely driven by EME ITers However, industrial ITers (even industrial stationary ITers) still exhibit a worse macro performance and monetary plicy efficiency than what NITers do (see figures 5.5c and 5.6c).

23 6. Inflation Accuracy We follow previous work (Calderón and Schmidt-Hebbel 2003, Albagli and Schmidt-Hebbel 2005) to measure inflation deviations and estimate their determinants. Here we extend the latter research (which was applied to ITers only) to compute and estimate significant differences between inflation deviations from targets (or from inflation trends) in IT countries and deviations from inflation trends in NIT countries. Method: Compute absolute differences between inflation levels and observed targets (for ITers) or between inflation levels and unobserved inflation trends (for ITers and NITers).

24 6. Inflation Accuracy Estimate significant differences in deviations from inflation targets and/or inflation trends between different pairs of IT and NIT country groups, controlling for inflation shocks and the output gap, based on panel IV (instrumentalizing endogenous variables), with country and time-specific effects, based on the following equation: (6.1) Perform robustness tests of results for: –inflation deviations from targets and from HP trends for ITers –output gap and output growth as controls –different HP filter smoothing parameters used in the HP estimation of inflation trends, –various country groups of ITers and NITers.

25 6. Inflation Accuracy Data: unconditional medians of inflation deviations from targets in IT countries (or from inflation trends in IT countries) are larger than unconditional medians of inflation deviations from inflation trends in NIT countries (see Table 6.1) ID 1ID 2

26 6. Inflation Accuracy However, regression results that include relevant controls suggest that absolute inflation deviations are smaller in IT countries than in NIT countries, when using all NITers and the pre-IT countries as control group (not signific. results for control group is all NITers): ITers – NITers long-term difference in absolute inflation deviation measured as: deviation from targets for ITers deviation from HP trends for ITers All ITers -0.4%-0.6% Stationary ITers -0.3%-0.6% Converging ITers -0.5% Industrial ITers zero-0.5% Emerging ITers -0.5%-0.6%

27 7. Concluding Remarks IT helps countries achieve over time: 1.lower inflation levels in the long run 2. smaller response to oil price and exchange rate shocks 3. stronger monetary independence 4. better macroeconomic performance (lower volatilities) and improved monetary policy efficiency 5. inflation levels closer to inflation objectives. Some benefits are larger after targets become stationary and certainly industrial-country ITers generally reflect larger gains and/or better performance than EME ITers. In general ITers do not do better than industrial-country NITers. However, industrial-country ITers perform at the level of our very demanding sample of 13 NITers.

28 Does Inflation Targeting Make a Difference? Klaus Schmidt-Hebbel Central Bank of Chile (jointly with Frederic S. Mishkin, Columbia University) 8 th Annual Meeting of the Central Bank of Brazil Rio de Janeiro, 11 August 2006