Optimal consumption under uncertainty, liquidity constraints, and bounded rationality

Ömer Özak

No 1307, Departmental Working Papers from Southern Methodist University, Department of Economics

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.

Keywords: Adaptive learning models; bounded rationality; dynamic programming; consumption function; behavioral economics; liquidity constraint; saving behavior (search for similar items in EconPapers)
JEL-codes: C6 D8 D9 E21 (search for similar items in EconPapers)
Date: 2013-09
New Economics Papers: this item is included in nep-cmp and nep-mac
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Optimal consumption under uncertainty, liquidity constraints, and bounded rationality (2014)
Working Paper: Optimal consumption under uncertainty, liquidity constraints, and bounded rationality (2012)
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