适应性学习预期如何合理化巴西更强的货币政策反应？（英）

How Do Adaptive Learning Expectations Rationalize Stronger Monetary Policy Response in Brazil?Allan Gloe Dizioli, Hou Wang WP/23/19 IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. 2023 JAN *1 International Monetary Fund Research Department. 700 19th St NW, Washington, DC 20431, USA. Corresponding author is [].© 2023 International Monetary Fund WP/23/19IMF Working Paper Research Department How Do Adaptive Learning Expectations Rationalize Stronger Monetary Policy Response in Brazil? Prepared by Allan Gloe Dizioli and Hou Wang* Authorized for distribution by Rafael Portillo January 2023IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. ABSTRACT: This paper estimates a standard Dynamic Stochastic General Equilibrium (DSGE) model that includes a wage and price Phillip's curves with different expectation formation processes for Brazil and the USA. Other than the standard rational expectation process, we also use a limited rationality process, the adaptive learning model. In this context, we show that the separate inclusion of a labor market in the model helps to anchor inflation even in a situation of adaptive expectations, a positive output gap and inflation above target. The estimation results show that the adaptive learning model does a better job in fitting the data in both Brazil and the USA. In addition, the estimation shows that expectations are more backward-looking and started to drift away sooner in 2021 in Brazil than in the USA. We then conduct optimal policy exercises that prescribe early monetary policy tightening in the context of positive output gaps and inflation far above the central bank target. RECOMMENDED CITATION: Dizioli, Allan, Hou Wang, 2023, “How Do Adaptive Learning Expectations Rationalize Stronger Monetary Policy Response in Brazil?”, IMF Working Papers JEL Classification Numbers: E2, E30, E52, E37, J6 Keywords: DSGE; Inflation dynamics; optimal monetary policy; Forecasting and Simulation; Bayesian estimation. Author’s E-Mail Address: adizioli@imf.org, HWang2@imf.org IMF WORKING PAPERS Wage-Price Spirals: What is the Historical Evidence? INTERNATIONAL MONETARY FUND 3 WORKING PAPERS How do adaptive learning expectations rationalize stronger monetary policy response in Brazil? Prepared by Allan Gloe Dizioli and Hou Wang1 1 We thank Rafael Portillo, Matteo Ghilardi, Romain Bouis, Woon Gyu Choi and Pierre Guerin for their useful comments. The views expressed in this Working Papers are those of the authors and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. 2ContentsPageI. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4II. Literature Review. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6III. Model environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8A. Workhorse model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8B. Expectation formation processes. . . . . . . . . . . . . . . . . . . . . . . . 10IV. Data and Model Estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12A. Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12B. Model fit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15V. Model simulation results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16A. Comparison of the estimation results for Brazil and the USA. . . . . . . . . . 16B. Adaptive learning expectations could increase the cost of stabilization. . . . . 18C. Optimal monetary policy discussion. . . . . . . . . . . . . . . . . . . . . . . 22VI. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Tables1. Prior Distribution of the Estimated Parameters. . . . . . . . . . . . . . . . . . . 142. Posterior Estimates for RE and AL Models. . . . . . . . . . . . . . . . . . . . . 153. In-Sample Forecast Performance for RE and AL Models. . . . . . . . . . . . . . 154. Out-of-Sample Forecast Performance for RE and AL Models. . . . . . . . . . . . 16Figures1. Brazil: Low wage workers suffered larger employment losses. . . . . . . . . . . . 132. Real composition-constant wages did not increase as much. . . . . . . . . . . . . 143. Brazil: Shock decomposition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184. U