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Markov Chain Monte Carlo Procedure to Generate Revealed Preference Consistent Datasets

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  • Thomas Demuynck

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.

Suggested Citation

  • Thomas Demuynck, 2020. "Markov Chain Monte Carlo Procedure to Generate Revealed Preference Consistent Datasets," Working Papers ECARES 2020-33, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:eca:wpaper:2013/310214
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    References listed on IDEAS

    as
    1. Varian, Hal R, 1982. "The Nonparametric Approach to Demand Analysis," Econometrica, Econometric Society, vol. 50(4), pages 945-973, July.
    2. Hal R. Varian, 1983. "Non-parametric Tests of Consumer Behaviour," Review of Economic Studies, Oxford University Press, vol. 50(1), pages 99-110.
    3. Chambers,Christopher P. & Echenique,Federico, 2016. "Revealed Preference Theory," Cambridge Books, Cambridge University Press, number 9781107087804.
    4. Ian Crawford & Bram De Rock, 2014. "Empirical Revealed Preference," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 503-524, August.
    5. Jan Heufer, 2014. "Generating Random Optimising Choices," Computational Economics, Springer;Society for Computational Economics, vol. 44(3), pages 295-305, October.
    6. Donald Brown & Caterina Calsamiglia, 2007. "The Nonparametric Approach to Applied Welfare Analysis," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 31(1), pages 183-188, April.
    7. Claude J. P. Bélisle & H. Edwin Romeijn & Robert L. Smith, 1993. "Hit-and-Run Algorithms for Generating Multivariate Distributions," Mathematics of Operations Research, INFORMS, vol. 18(2), pages 255-266, May.
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    More about this item

    Keywords

    Revealed preference; Markov Chain Monte Carlo; Bronars Power;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory

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