Efficeincy Gains in Rank-ordered Multinomial Logit Models
Arie Beresteanu ()
Authors registered in the RePEc Author Service: Federico Zincenko ()
No 5878, Working Paper from Department of Economics, University of Pittsburgh
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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