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Computing Revealed Preference Goodness of fit Measures with Integer Programming

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  • Thomas Demuynck
  • John Rehbeck

Abstract

This paper develops mixed-integer linear programming (MILP) formulations to compute various revealed preference goodness-of-fit measures. We provide MILP formulations to compute the Houtman-Maks Index, the Average Varian Index, and the Minimum Cost Index when there are linear budgets. Next, we provide MILPs to compute minimal measurement error in expenditures, prices, and quantities. Finally, we extend our results to non-linear budgets. As a proof of concept, we compute various goodness-of-fit measures for experimental choice data sets from the literature. The maximal computation time is less than 3 seconds for all measures examined on these datasets.

Suggested Citation

  • Thomas Demuynck & John Rehbeck, 2021. "Computing Revealed Preference Goodness of fit Measures with Integer Programming," Working Papers ECARES 2021-26, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:eca:wpaper:2013/334880
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    References listed on IDEAS

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    Cited by:

    1. Mir Adnan Mahmood & John Rehbeck, 2022. "Correcting for Random Budgets in Revealed Preference Experiments," Games, MDPI, vol. 13(2), pages 1-14, April.

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    Keywords

    Revealed preference; choice consistency; computation;
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