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A Rank‐Ordered Logit Model With Unobserved Heterogeneity In Ranking Capabilities

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Author Info

  • Dennis Fok
  • Richard Paap
  • Bram Van Dijk

Abstract

In this paper we consider the situation where one wants to study the preferences of individuals over a discrete choice set through a survey. In the classical setup respondents are asked to select their most preferred option out of a (selected) set of alternatives. It is well known that, in theory, more information can be obtained if respondents are asked to rank the set of alternatives instead. In statistical terms, the preferences can then be estimated more efficiently. However, when individuals are unable to perform (part of) this ranking task, using the complete ranking may lead to a substantial bias in parameter estimates. In practice, one usually opts to only use a part of the reported ranking. In this paper we introduce a latent-class rank-ordered logit model in which we use latent segments to endogenously identify the ranking capabilities of individuals. Each segment corresponds to a different assumption on the ranking capability. Using simulations and an empirical application, we show that using this model for parameter estimation results in a clear efficiency gain over a multinomial logit model in case some individuals are able to rank. At the same time it does not suffer from biases due to ranking inabilities of some of the respondents.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 27 (2012)
Issue (Month): 5 (08)
Pages: 831-846

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Handle: RePEc:wly:japmet:v:27:y:2012:i:5:p:831-846

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  1. Darrell R. Mark & Jayson L. Lusk & M. Scott Daniel, 2004. "Recruiting Agricultural Economics Graduate Students: Student Demand for Program Attributes," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(1), pages 175-184.
  2. Wolak, Frank A., 1989. "Local and Global Testing of Linear and Nonlinear Inequality Constraints in Nonlinear Econometric Models," Econometric Theory, Cambridge University Press, vol. 5(01), pages 1-35, April.
  3. Koop, G & Poirier, D J, 1994. "Rank-Ordered Logit Models: An Empirical Analysis of Ontario Voter Preferences," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 9(4), pages 369-88, Oct.-Dec..
  4. Hausman, Jerry A. & Ruud, Paul A., 1987. "Specifying and testing econometric models for rank-ordered data," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 83-104.
  5. Wolak, Frank A., 1989. "Testing inequality constraints in linear econometric models," Journal of Econometrics, Elsevier, vol. 41(2), pages 205-235, June.
  6. McFadden, Daniel, 1974. "The measurement of urban travel demand," Journal of Public Economics, Elsevier, vol. 3(4), pages 303-328, November.
  7. van Ophem, Hans & Stam, Piet & Van Praag, Bernard M S, 1999. "Multichoice Logit: Modeling Incomplete Preference Rankings of Classical Concerts," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 117-28, January.
  8. Jerry Hausman, 2001. "Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 57-67, Fall.
  9. Beggs, S. & Cardell, S. & Hausman, J., 1981. "Assessing the potential demand for electric cars," Journal of Econometrics, Elsevier, vol. 17(1), pages 1-19, September.
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Cited by:
  1. Yoo, Hong Il & Doiron, Denise, 2013. "The use of alternative preference elicitation methods in complex discrete choice experiments," Journal of Health Economics, Elsevier, vol. 32(6), pages 1166-1179.

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