Computing Revealed Preference Goodness of fit Measures with Integer Programming

Thomas Demuynck and John Rehbeck

No 2021-26, Working Papers ECARES from ULB -- Universite Libre de Bruxelles

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.

Keywords: Revealed preference; choice consistency; computation (search for similar items in EconPapers)
Pages: 27 p.
Date: 2021-11
New Economics Papers: this item is included in nep-dcm
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Computing revealed preference goodness-of-fit measures with integer programming (2023)
Working Paper: Computing Revealed Preference Goodness of fit Measures with Integer Programming (2023)
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