Local Rationality

A new behavioral concept, local rationality, is developed within the context of a simple heterogeneous-agent model with incomplete markets. To make savings decisions, agents forecast the shadow price of asset holdings. Absent aggregate uncertainty, locally rational agents forecast shadow prices rationally, and thereby make optimal state-contingent decisions. They use adaptive learning to extend their forecasts to accommodate aggregate uncertainty. Over time the state evolves to an ergodic distribution centered near the economy’s restricted perceptions equilibrium. To examine the dynamics implied by our behavioral assumptions, we conduct a calibration exercise. As is well-known, in a calibrated representative-agent RBC model the volatility of consumption is too low relative to the data. Extending the model by either incorporating adaptive learning or heterogeneous agents fails to alter this conclusion. We find that local rationality, which interacts heterogeneity and adaptive learning, significantly improves the model’s fit along this dimension