Local Rationality

37 Pages Posted: 23 Apr 2022

See all articles by David Evans

David Evans

University of Oregon

Jungang Li

University of Oregon

Bruce McGough

University of Oregon

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 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.

Keywords: bounded rationality, real business cycles, heterogenous agent models, Adaptive Learning

Suggested Citation

Evans, David and Li, Jungang and McGough, Bruce, Local Rationality. Available at SSRN: https://ssrn.com/abstract=4091214 or http://dx.doi.org/10.2139/ssrn.4091214

David Evans

University of Oregon ( email )

1280 University of Oregon
Eugene, OR 97403
United States

Jungang Li

University of Oregon ( email )

1280 University of Oregon
Eugene, OR 97403
United States

Bruce McGough (Contact Author)

University of Oregon ( email )

1280 University of Oregon
Eugene, OR 97403
United States

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