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Transcript
Inflation Targeting in Emerging Market Economies Arminio Fraga Ilan Goldfajn André Minella Preliminary Version April 2003 Comments are Welcome 1. Introduction Motivation Inflation targeting (IT) is doing well in general, although a bit less so in emerging market economies (EMEs) Paper looks at EMEs and asks what have we learned In a way, our experience in Brazil can be seen as a stress test of IT! Inflation Before and After the Adoption of Inflation Targeting Figure 1 Inflation Befor e and After Adoption of Inflation Tar geting (IT) 13.11 Emergin g Market Econom ies 14 12 10 8 5.95 % Developed Eco nomies 6 3.72 2.50 4 2 0 Before IT After IT (until 2002:2) Before IT After IT (until 2002:2) Main issues EMEs seem to face a lot of shocks, large shocks Some of them may be endogenous due to weak institutions, historical problems, etc. We discuss mainly how to manage monetary policy when confronted with shocks. Other policy recommendations are implicit or briefly discussed Key issue: credibility versus flexibility We discuss the role of a communication strategy, of bands, horizons, etc. 2. Stylized facts about inflation targeting in EME Higher volatility of inflation, GDP growth, interest rate and exchange rate Higher inflation level Comparison of volatilities Figure 2 Volatilities of Selected Variables of the period 1997-2002 (Average of Standard Deviation) Interest Rate 6 5.47 5 GDP Growth Inflatio n 4 3.40 3.37 3 2.07 2 Exchange Rate (Standard Deviation/Average) 1.24 1.27 1 0.11 0.15 0 DE EME DE EME DE EME DE EME Trade-off Volatilities F ig ure 3 Trade-o ff Volatilities Outpu t and Inflatio n (1997:1 - 2002:2) 7.0 6.5 Korea Thailand Standard Deviation of GDP Growth 6.0 5.5 5.0 4.5 4.0 New Zealand 3.5 Iceland Chile Peru Norway Sweden 2.0 Average D E 1.5 Colombia Israel 3.0 2.5 Average EME Mexico Czech Republic Poland Brazil Australia Canada 1.0 Switzerland South Africa United Kingdom 0.5 0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 Standard Deviation of In flation 5.0 5.5 6.0 6 .5 7.0 7 .5 3. Model Macro model for simulation Small open economy Combines features of Batini, Harrison, and Millard’s (2001), and McCallum and Nelson’s (2001) formulations Derived from the intertemporal optimization of households and firms Price rigidity 4. Why is volatility higher? Credibility building and disinflationary needs Dominance issues: financial and fiscal Larger shocks 4.1. Credibility building and disinflationary needs Both cases appear in the old macro literature: adaptive expectations and inertia (or persistence) IT is an attempt to accelerate the process of building credibility Inflation before IT adoption Figure 5 12-Month Inflation Right Before IT Adoption 45 Peru 40 35 30 Chile % 25 Mexico 20 Israel 15 Poland 10 New Zealand Czech Republic Canada South Korea 5 United Kingdom Sweden 0 Dec-88 May-90 Sep-91 Australia Jan-93 Jun-94 Developed Economies Oct-95 Mar-97 Hungary Colombia Iceland Brazil South Africa Norway Switzerland Thailand Jul-98 Emerging Market Economies Dec-99 Apr-01 Sep-02 Inflation Target Averages Figure 7 Inflation Target Averages - Central Points 20 18 16 14 % 12 10 8 6 4 2 0 1990 1991 1992 1993 1994 1995 Emerging Market Economies 1996 1997 1998 1999 Developed Economies 2000 2001 2002 Brazilian case: Central Bank’s reaction function Dependent Variable: Selic Interest Rate (Monthly Average) Coefficients and standard errors Regressors Regression with Inflation Report Inflation Expectations Regression with Market Inflation Expectations Constant 17.57*** (0.48) 16.68*** (0.69) Interest Rate (t-1) 1.04*** (0.13) 1.36*** (0.18) Interest Rate (t-2) -0.20** (0.08) -0.56*** (0.15) p 1.84 (1.19) 1.42* (0.72) -0.47*** (0.16) -0.13 (0.17) R-squared 0.9418 0.9539 Adjusted R-squared 0.9342 0.9465 LM Test for Autocorrelation of Residuals (p-values) 1 lag 0.5186 0.7408 4 lags 0.6766 0.5612 Deviation of Expected Inflation Rate from Target Output Gap (t-1) Notes: Standard error in parantheses. *, **, and *** indicate the coefficient is significant at 10%, 5%, and 1% level, respectively. P refers to a p-value of 0.13. For the regression with Inflation Report inflation expectations the sample is 1999:06-2002:06, and for that with market inflation expectations the sample is 2000:01-2002:06. Brazilian case: Aggregate supply curve Table 5 Estimation of Aggregate Supply Curve for Brazil Dependent Variable: Monthly Inflation Rate Regressors Constant Coefficients and standard errors 0.79** (0.32) Dummy*Constant 0.39*** (0.14) Inflation Rate (t-1) 0.61*** (0.13) Dummy*Inflation Rate(t-1) -0.41** (0.19) Inflation Rate (t-2) -0.09 (0.12) Dummy*Inflation Rate (t-2) -0.24 (0.19) Unemployment (t-1) -0.10** (0.05) Exchange Rate Change (t-1) (Twelve-Month Average) 0.10*** (0.04) Dummy for 2000:07 1.08*** (0.33) R-squared 0.5537 Adjusted R-squared 0.5055 LM Test for Autocorrelation of Residuals (p-values) 1 lag 0.5454 4 lags 0.1081 Notes: Standard error in parantheses. *, **, and *** indicate the coefficient is significant at 10%, 5%, and 1% level, respectively. Sample: 1995:08 - 2002:06 (the sample starts 13 months after the stabilization because we are using 12-month exchange rate change). Dummy refers to the inflation-targeting period (1999:07-2002:06), unless otherwise noticed. 4.2. Dominance issues General: central bank will/may inflate Fiscal dominance Financial dominance External dominance (sub-investment grade) 4.3. Shocks and “sudden stops” Exchange rate volatiliy Importance as shock factor 5. How to deal with higher volatility Answer: good communications and a high degree of transparency 5.1. Target bands, horizons and core In a perfect world bands/horizons have no role: central bank responds optimally given exact size and nature of shock, parameters of the economy, and preferences concerning inflation So, what, if any, is their role? Bands: signalling/check point (focus on point target) Horizon: seems arbitrary Core: no really good measure, confusing Calendar year issues 5.2. Monetary policy committees, meeting minutes, and inflation reports Existence of a monetary policy committee (MPC) Timely publication of detailed minutes of MPC meetings Quarterly Inflation Report 5.3. Shocks and adjusted targets: the case of Brazil 1. 2. 3. Methodology Compute shocks (supply shocks) - include future path Accommodate direct impact, i.e., announce an adjusted or intermediate target (path) The chosen path will be a function of parameters of the economy (e.g., inertia) and inflation aversion Brazil: Adjusted target 2003 Adjusted targets for 2003 and 2004 Itemization 2003 2004 (a) Target for inflation set by CMN 4.0 3.75 (b) Regulated-price shocks1 1.7 1.1 (c) Inertia to be fought in following years2 2.8 0.6 4.2 1.0 of regulated prices 1.4 0.4 of market prices 2.8 0.6 8.5 5.5 Inherit inertia from the previous year (total) (d) Adjusted targets (=(a)+(b)+(c)) 1) For the calculation of the shock, the effect of inertia and exchange rate on regulated-price inflation is deducted. 2) The inertia to be fought in the following years corresponds to 2/3 of the inertia inherited from the previous year. 5.4. IT and IMF programs Net domestic assets (NDA) or monetary aggregates targeting makes little sense Forward-looking quarterly targets with consultation bands was the solution we found 6. Conclusions To deal with this more volatile environment, we recommend: High degree of transparency and a good communications strategy A methodology to calculate the convergence path following a shock (adjusted targets) Better IMF conditionality under inflation targeting