Optimal consumption under uncertainty, liquidity constraints, and bounded rationality

Authored by Oemer Oezak

Date Published: 2014-02

DOI: 10.1016/j.jedc.2013.12.007

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

I study how boundedly rational agents can learn a “good” solution to an infinite horizon optimal consumption problem under uncertainty and liquidity constraints. Using an empirically plausible theory of learning I propose a class of adaptive learning algorithms that agents might use to choose a consumption rule. I show that the algorithm always has a globally asymptotically stable consumption rule, which is optimal. Additionally, I present extensions of the model to finite horizon settings, where agents have finite lives and life-cycle income patterns. This provides a simple and parsimonious model of consumption for large agent based models. (C) 2013 Elsevier B.V. All rights reserved.

Tags

Bounded rationality Behavioral economics Adaptive learning models Consumption function Dynamic programming Saving behavior