| Title: |
Constrained data-fitters |
| Authors: |
Samuelson, Larry; Steiner, Jakub |
| Source: |
Samuelson, Larry; Steiner, Jakub (2024). Constrained data-fitters. Working paper series / Department of Economics 460, University of Zurich. |
| Publication Year: |
2024 |
| Collection: |
University of Zurich (UZH): ZORA (Zurich Open Repository and Archive |
| Subject Terms: |
Department of Economics; 330 Economics; Bayesian updating; cognitive constraints; belief formation; machine learning in economics; Bayesian networks |
| Description: |
We study maximum-likelihood estimation and updating, subject to computational, cognitive, or behavioral constraints. We jointly characterize 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, simple models yield the most effective constrained ft 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. |
| Document Type: |
report |
| File Description: |
application/pdf |
| Language: |
English |
| ISSN: |
1664-7041 |
| Relation: |
https://www.zora.uzh.ch/id/eprint/264855/1/econwp460.pdf; urn:issn:1664-7041 |
| Availability: |
https://www.zora.uzh.ch/id/eprint/264855/; https://www.zora.uzh.ch/id/eprint/264855/1/econwp460.pdf |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.7F61F53F |
| Database: |
BASE |