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Constrained data-fitters

Author

Listed:
  • Larry Samuelson
  • Jakub Steiner

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.

Suggested Citation

  • Larry Samuelson & Jakub Steiner, 2024. "Constrained data-fitters," ECON - Working Papers 460, Department of Economics - University of Zurich.
  • Handle: RePEc:zur:econwp:460
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    File URL: https://www.zora.uzh.ch/id/eprint/264855/1/econwp460.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Bayesian updating; cognitive constraints; belief formation; machine learning in economics; Bayesian networks;
    All these keywords.

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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