Macroeconomic Learning and the Propagation of Technology Shocks

Abstract

We incorporate learning in a standard dynamic stochastic general equilibrium model along two empirically-supported dimensions. First, we assume that agents cannot directly observe the individual components of the productivity shock and instead must conduct signal extraction exercises and update beliefs about the source of aggregate shocks. For this type of learning to have qualitative impacts on dynamic behavior we must assume either a counterfactually high relative variance or a large persistence parameter on the trend component of productivity. Second, we propose an alternative learning mechanism in which technological innovations diffuse slowly through the economy. This mechanism is successful at generating a variety of empirically observed responses under reasonable parameterizations.