Learning integrated inflation forecasts in a simple multi-agent macroeconomic model

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This paper implements a model with a population of heterogeneous macro forecasters. Their objectives are to forecast output and inflation, both inputs in standard New Keynesian macro models. The model is implemented by first calibrating the agents to professional forecasters at the micro level. Model runs then try to replicate both the dynamics, bias and cross sectional heterogeneity of forecasts and the economy. These are done both in a model with static forecasters, and one where the forecasters are learning from each other in a social fashion. We find that expectations about the inflation process which conjecture near random walk behavior can be self-fulfilling, yielding inflation volatility and persistence on the order of magnitude of U.S. macro data. However, our forecasting populations often fall short of the heterogeneity of predictions from survey data. In some cases, monetary policy can be used to shift the model from its volatile/persistent equilibrium over to a more stable, strongly mean reverting inflation rate.