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

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