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The Empirical Content of Binary Choice Models

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  • Bhattacharya, D.

Abstract

Empirical demand models used for counterfactual predictions and welfare analysis must be rationalizable, i.e. theoretically consistent with utility maximization by heterogeneous consumers. We show that for binary choice under general unobserved heterogeneity, rationalizability is equivalent to a pair of Slutsky-like shape-restrictions on choice-probability functions.The forms of these restrictions differ from Slutsky-inequalities for continuous goods. Unlike McFadden-Richter's stochastic revealed preference, our shape-restrictions (a) are global, i.e. their forms do not depend on which and how many budget-sets are observed, (b) are closed-form, hence easy to impose on parametric/semi/non-parametric models in practical applications, and (c) provide computationally simple, theory-consistent bounds on demand and welfare predictions on counterfactual budget-sets.

Suggested Citation

  • Bhattacharya, D., 2018. "The Empirical Content of Binary Choice Models," Cambridge Working Papers in Economics 1883, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:1883
    Note: db692
    as

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    References listed on IDEAS

    as
    1. Matzkin, Rosa L, 1992. "Nonparametric and Distribution-Free Estimation of the Binary Threshold Crossing and the Binary Choice Models," Econometrica, Econometric Society, vol. 60(2), pages 239-270, March.
    2. Dette, Holger & Hoderlein, Stefan & Neumeyer, Natalie, 2016. "Testing multivariate economic restrictions using quantiles: The example of Slutsky negative semidefiniteness," Journal of Econometrics, Elsevier, vol. 191(1), pages 129-144.
    3. Yuichi Kitamura & Jörg Stoye, 2018. "Nonparametric Analysis of Random Utility Models," Econometrica, Econometric Society, vol. 86(6), pages 1883-1909, November.
    4. repec:wly:quante:v:9:y:2018:i:2:p:571-615 is not listed on IDEAS
    5. Debopam Bhattacharya, 2015. "Nonparametric Welfare Analysis for Discrete Choice," Econometrica, Econometric Society, vol. 83, pages 617-649, March.
    6. Arthur Lewbel, 2001. "Demand Systems with and without Errors," American Economic Review, American Economic Association, vol. 91(3), pages 611-618, June.
    7. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    8. Jerry A. Hausman & Whitney K. Newey, 2016. "Individual Heterogeneity and Average Welfare," Econometrica, Econometric Society, vol. 84, pages 1225-1248, May.
    9. Daniel McFadden, 2005. "Revealed stochastic preference: a synthesis," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 26(2), pages 245-264, August.
    10. Bhattacharya, Debopam, 2008. "A Permutation-Based Estimator For Monotone Index Models," Econometric Theory, Cambridge University Press, vol. 24(3), pages 795-807, June.
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    More about this item

    Keywords

    Binary choice; general heterogeneity; income effect; utility maximization; integrability/rationalizability; Slutsky inequality; shape-restrictions;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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