Efficiency Gains in Rankâ€ ordered Multinomial Logit Models
Arie Beresteanu () and
Federico Zincenko ()
Oxford Bulletin of Economics and Statistics, 2018, vol. 80, issue 1, 122-134
This paper considers estimation of discrete choice models when agents report their ranking of the alternatives (or some of them) rather than just the utility maximizing alternative. We investigate the parametric conditional rankâ€ ordered Logit model. We show that conditions for identification do not change even if we observe ranking. Moreover, we fill a gap in the literature and show analytically and by Monte Carlo simulations that efficiency increases as we use additional information on the ranking.
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Persistent link: https://EconPapers.repec.org/RePEc:bla:obuest:v:80:y:2018:i:1:p:122-134
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