Constrained data-fitters

Larry Samuelson and Jakub Steiner

No 460, ECON - Working Papers from Department of Economics - University of Zurich

Abstract: We study maximum-likelihood estimation and updating, subject to computational, cognitive, or behavioral constraints. We jointly character- ize constrained estimates and updating within a framework reminiscent of a machine learning algorithm. Without frictions, the framework simplifies to standard maximum-likelihood estimation and Bayesian updating. Our central finding is that under certain intuitive cognitive constraints, sim- ple models yield the most e ective constrained fit to data|more complex models offer a superior fit, but the agent may lack the capability to assess this fit accurately. With some additional structure, the agent's problem is isomorphic to a familiar rational inattention problem.

Keywords: Bayesian updating; cognitive constraints; belief formation; machine learning in economics; Bayesian networks (search for similar items in EconPapers)
JEL-codes: D8 (search for similar items in EconPapers)
Date: 2024-11
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