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

  • Hong il Yoo

    ()

    (University of New South Wales)

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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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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  1. John Calfee & Clifford Winston & Randolph Stempski, 2001. "Econometric Issues In Estimating Consumer Preferences From Stated Preference Data: A Case Study Of The Value Of Automobile Travel Time," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 699-707, November.
  2. Joel Huber and Kenneth Train., 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Economics Working Papers E00-289, University of California at Berkeley.
  3. 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. Siikamaki, Juha & Layton, David F., 2007. "Discrete choice survey experiments: A comparison using flexible methods," Journal of Environmental Economics and Management, Elsevier, vol. 53(1), pages 122-139, January.
  6. 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.
  7. 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.
  8. 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.
  9. Alejandro Caparr�s & José L. Oviedo & Pablo Campos, 2008. "Would You Choose Your Preferred Option? Comparing Choice and Recoded Ranking Experiments," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(3), pages 843-855.
  10. Kenneth E. Train & Clifford Winston, 2007. "Vehicle Choice Behavior And The Declining Market Share Of U.S. Automakers," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1469-1496, November.
  11. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
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