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Generating Random Optimising Choices

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  • Jan Heufer

Abstract

We provide an efficient way to generate random choices which are consistent with utility maximisation. They are drawn from an approximate uniform distribution on the admissible region on each budget based on a Markovian Monte Carlo algorithm due to Smith (Oper Res 32(6):1296–1308, 1984 ). This can be used to extend Bronars’ (Econometrica 55(3):693–698, 1987 ) method by approximating the power of tests for conditions for which utility maximisation is necessary but not sufficient (e.g., homotheticity, separability, etc.). The approach can also be applied to production analysis. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Jan Heufer, 2014. "Generating Random Optimising Choices," Computational Economics, Springer;Society for Computational Economics, vol. 44(3), pages 295-305, October.
    • Handle: RePEc:kap:compec:v:44:y:2014:i:3:p:295-305
    DOI: 10.1007/s10614-013-9393-8
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    References listed on IDEAS

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    1. Varian, Hal R, 1982. "The Nonparametric Approach to Demand Analysis," Econometrica, Econometric Society, vol. 50(4), pages 945-973, July.
    2. Raymond Fisman & Shachar Kariv & Daniel Markovits, 2007. "Individual Preferences for Giving," American Economic Review, American Economic Association, vol. 97(5), pages 1858-1876, December.
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    4. Robert L. Smith, 1984. "Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed over Bounded Regions," Operations Research, INFORMS, vol. 32(6), pages 1296-1308, December.
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    8. Heufer, Jan, 2011. "Stochastic Dominance and Nonparametric Comparative Revealed Risk Aversion," Ruhr Economic Papers 289, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    9. Little, James T., 1979. "Indirect preferences," Journal of Economic Theory, Elsevier, vol. 20(2), pages 182-193, April.
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    Cited by:

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    3. Hjertstrand, Per, 2021. "Power against random expenditure allocation for revealed preference tests," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 36-45.
    4. Hengzhen Lu & Yingying Zhang & Ling Xiao & Gurjeet Dhesi, 2022. "A State-of-the-Art Fund Performance Index: Higher-Order Omega and Its Consistency with Almost Stochastic Dominance," JRFM, MDPI, vol. 15(10), pages 1-20, September.

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