Local rationality

Abstract

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 agentsforecast 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 near the economy’s restricted perceptions equilibrium. In a partial equilibrium environment we develop intuition for locally rational decision making, documenting an important hysteresis effect. General equilibrium dynamics are examined via a calibration exercise. Calibrated representative-agent RBC models induce low consumption volatility relative to the data. Extending the model by either incorporating adaptive learning or heterogeneous agents fails to alter this conclusion. Via the hysteresis effect, local rationality, which interacts heterogeneity and adaptive learning, significantly improves the model’s fit along this dimension.