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The perceived unreliability of rank-ordered data: an econometric origin and implications

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  • Hong il Yoo

    ()
    (University of New South Wales)

Abstract

The problem of unstable coecients in the rank-ordered logit model has been traditionally interpreted as a sign that survey respondents fail to provide reliable ranking responses. This paper shows that the problem may embody the inherent sensitivity of the model to stochastic misspecification instead. Even a minor departure from the postulated random utility function can induce the problem, for instance when rank-ordered logit is estimated whereas the true additive disturbance is iid normal over alternatives. Related implications for substantive analyses and further modelling are explored. In general, a well-speci ed random coecient rank-ordered logit model can mitigate, though not eliminate, the problem and produce analytically useful results. The model can also be generalised to be more suitable for forecasting purposes, by accommodating that stochastic misspecification matters less for individuals with more deterministic preferences. An empirical analysis using an Australian nursing job preferences survey shows that the estimates behave in accordance with these implications.

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File URL: http://research.economics.unsw.edu.au/RePEc/papers/2012-46.pdf
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Bibliographic Info

Paper provided by School of Economics, The University of New South Wales in its series Discussion Papers with number 2012-46.

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Length: 42 pages
Date of creation: Nov 2012
Date of revision:
Handle: RePEc:swe:wpaper:2012-46

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Keywords: rank-ordered logit; mixed logit; latent class; stated ranking;

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  1. Huber, Joel & Train, Kenneth, 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Department of Economics, Working Paper Series qt7zm4f51b, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
  2. Layton, David F., 2000. "Random Coefficient Models for Stated Preference Surveys," Journal of Environmental Economics and Management, Elsevier, vol. 40(1), pages 21-36, July.
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  5. Michael Keane & Nada Wasi, 2013. "Comparing Alternative Models Of Heterogeneity In Consumer Choice Behavior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 1018-1045, 09.
  6. Hong il Yoo & Denise Doiron, 2012. "The use of alternative preference elicitation methods in complex discrete choice experiments," Discussion Papers 2012-16, School of Economics, The University of New South Wales.
  7. Riccardo Scarpa & Sandra Notaro & Jordan Louviere & Roberta Raffaelli, 2010. "Exploring Scale Effects of Best/Worst Rank Ordered Choice Data to Estimate Benefits of Tourism in Alpine Grazing Commons," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(3), pages 809-824.
  8. Ben-Akiva, Moshe & Morikawa, Takayuki & Shiroishi, Fumiaki, 1992. "Analysis of the reliability of preference ranking data," Journal of Business Research, Elsevier, vol. 24(2), pages 149-164, March.
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  11. Train, Kenneth E. & Winston, Clifford, 2006. "Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers," Working paper 331, Regulation2point0.
  12. 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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