We review and evaluate methods previously adopted in the applied literature of adaptive learning in order to initialize agents' beliefs. Previous methods are classified into three broad classes: equilibrium-related, training sample-based, and estimation-based. We conduct several simulations...

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...

We investigate the determinants of the commodity (ex-ante) risk premia for different maturities through the lens of a model of adaptive learning in which expected future spot prices are revised based on past prediction errors and changes in economic fundamentals. The main results show that risk...

Adaptive learning under constant-gain allows persistent deviations of beliefs from equilibrium so as to more realistically reflect agents' attempt of tracking the continuous evolution of the economy. A characterization of these beliefs is therefore paramount to a proper understanding of the role...

Adaptive learning introduces persistence in the evolution of agents’ beliefs over time. For applied purposes this is a convenient feature to help explain why economies present sluggish adjustments towards equilibrium. The pace of learning is directly determined by the gain parameter, which...

We compare forecasts from different adaptive learning algorithms and calibrations applied to US real-time data on inflation and growth. We find that the Least Squares with constant gains adjusted to match (past) survey forecasts provides the best overall performance both in terms of forecasting...

The paper studies how a prolonged period of subdued price developments may induce a de-anchoring of inflation expectations from the central bank's objective. This is shown within a framework where agents form expectations using adaptive learning, choosing among a set of alternative forecasting...

Under adaptive learning, recursive algorithms are proposed to represent how agents update their beliefs over time. For applied purposes these algorithms require initial estimates of agents perceived law of motion. Obtaining appropriate initial estimates can become prohibitive within the usual...

The present paper studies the effect of monetary policy on inflation and output within a New Keynesian model with Experience-Based Learning (EBL) that renders expectations heterogeneous across age groups. Under EBL, the age-distribution directly affects the composition of aggregate expectations...

This paper proposes an heterogenous asset pricing model in which different classes of investors coexist and evolve, switching among strategies over time according to a fitness measure. In the presence of boundedly rational agents, with biased forecasts and trend following rules, rational or...