Markov Chain Monte Carlo Procedure to Generate Revealed Preference Consistent Datasets

Thomas Demuynck

No 2020-33, Working Papers ECARES from ULB -- Universite Libre de Bruxelles

Abstract: This paper presents a Markov-Chain-Monte-Carlo (MCMC) procedure to sample uniformly from the collection of datasets that satisfy some revealed preference test. The MCMC combines a Gibbs-sampler with a simple hit and run step. It is shown that the MCMC has the uniform distribution as its unique invariant distribution and that it converges to this distribution at an exponential rate.

Keywords: Revealed preference; Markov Chain Monte Carlo; Bronars Power (search for similar items in EconPapers)
JEL-codes: C63 D11 (search for similar items in EconPapers)
Pages: 35 p.
Date: 2020-08
New Economics Papers: this item is included in nep-dcm and nep-ore
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Related works:
Journal Article: A Markov Chain Monte Carlo procedure to generate revealed preference consistent datasets (2021)
Working Paper: A Markov Chain Monte Carlo procedure to generate revealed preference consistent datasets (2021)
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