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The seeming unreliability of rank-ordered data as a consequence of model misspecification

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  • Yan, Jin
  • Yoo, Hong Il

Abstract

The rank-ordered logit model's coefficients often vary significantly with the depth of rankings used in the estimation process. The common interpretation of the unstable coefficients across ranks is that survey respondents state their more and less preferred alternatives in an incoherent manner. We point out another source of the same empirical regularity: stochastic misspecification of the random utility function. An example is provided to show how the well-known symptoms of incoherent ranking behavior can result from stochastic misspecification, followed by Monte Carlo evidence. Our finding implies that the empirical regularity can be addressed by the development of robust estimation methods.

Suggested Citation

  • Yan, Jin & Yoo, Hong Il, 2014. "The seeming unreliability of rank-ordered data as a consequence of model misspecification," MPRA Paper 56285, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:56285
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    File URL: https://mpra.ub.uni-muenchen.de/56285/1/MPRA_paper_56285.pdf
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    Cited by:

    1. Jin Yan & Hong Il Yoo, 2017. "Semiparametric Estimation of the Random Utility Model with Rank-Ordered Choice Data," Working Papers 2017_02, Durham University Business School.

    More about this item

    Keywords

    rank-ordered logit; exploded logit; ranking; qualitative response; stated preference;

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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