A Frequentist Approach to Revealed Preference Analysis

Charles Gauthier, Raghav Malhotra and Agustin Troccoli Moretti

Papers from arXiv.org

Abstract: This paper develops a framework to study the statistical power of revealed-preference tests. With randomly sampled budgets and mild smoothness of demand, statistical learning implies that any model consistent with the data must approximate true choice behaviour. We interpret this result as follows: passing a revealed-preference test is informative only to the extent that the data are sufficiently rich to rule out economically meaningful departures from the maintained model. We make this precise by linking sample size and confidence to the magnitude of detectable departures, and by characterising how power rises with additional observations. Extending our approach beyond revealed-preference inequalities to smooth functional restrictions yields practical tests, even when exact revealed-preference tests are computationally infeasible. We also provide confidence intervals for smooth functionals of demand, including welfare effects. Simulations show that standard sample sizes can generate widely different power across models, contextualizing why some conditions ``rarely reject'' in practice.

Date: 2022-08, Revised 2026-02
New Economics Papers: this item is included in nep-dcm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2208.03737 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2208.03737

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().