How do adaptive learning expectations rationalize stronger monetary policy response in Brazil?

Allan Dizioli and Hou Wang

Latin American Journal of Central Banking (previously Monetaria), 2024, vol. 5, issue 1

Abstract: This paper estimates a standard Dynamic Stochastic General Equilibrium (DSGE) model that includes a wage and price Phillips 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 adaptative 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 Brazil. 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 front-loading monetary policy tightening and easing earlier than the estimated monetary policy rule in the context of positive output gaps and inflation far above the central bank target.

Keywords: DSGE; Inflation dynamics; Optimal monetary policy; Forecasting and simulation; Bayesian estimation (search for similar items in EconPapers)
JEL-codes: E2 E30 E37 E52 J6 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:lajcba:v:5:y:2024:i:1:s2666143824000012

DOI: 10.1016/j.latcb.2024.100119

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