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Efficeincy Gains in Rank-ordered Multinomial Logit Models

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This paper considers estimation of discrete choice models when agents report their rankingof the alternatives (or some of them) rather than just the utility maximizing alternative. Weinvestigate the parametric conditional rank-ordered Logit model. We show that conditionsfor identifi cation do not change even if we observe ranking. Moreover, we ll a gap in theliterature and show analytically and by Monte Carlo simulations that efficiency increases as weuse additional information on the ranking.

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  • Arie Beresteanu, 2016. "Efficeincy Gains in Rank-ordered Multinomial Logit Models," Working Paper 5878, Department of Economics, University of Pittsburgh.
  • Handle: RePEc:pit:wpaper:5878
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    File URL: http://www.econ.pitt.edu/sites/default/files/working_papers/WP16-018.pdf
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    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, Fall.
    2. Dennis Fok & Richard Paap & Bram Van Dijk, 2012. "A Rankā€Ordered Logit Model With Unobserved Heterogeneity In Ranking Capabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 831-846, August.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, March.
    4. 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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