THE ESTIMATION OF DISCRETE CHOICE MODELS WITH LARGE CHOICE SET

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概要

• 論文の詳細を見る

• In this paper we discussed the estimation of choice model with correlation and/or heteroscedasticity across alternatives when the number of alternatives was large. We examined the performance of randomly drawn choice set approach and Poisson regression approach. We designed 6 Monte Carlo experiments. 1000 observations with 30 alternatives each were generated for each experiment. We found that the randomly drawn choice set approach is only applicable to independent and homoscedastic alternatives. When there are correlated factors among alternatives, this approach can not get good fit for the correlated factors even when the number of draw is quite large. We proved theoretically and empirically that Poisson regression model can get the same estimates as some logit type models that showed heteroscedasticity and correlation across alternatives. Poisson regression model can also get estimates close to those of logit kernel model with less computing time.

• Eastern Asia Society for Transportation Studiesの論文

著者

• Chen Yu-Chin

  Department of Microbiology, Soochow University

• HU Wan-Pei

  Department of Transportation and Communication Management Science National Cheng Kung University

• DUANN Liang-Shyong

  Department of Marketing and Logistics Southern Taiwan University of Technology

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• THE FINITE-SAMPLE PROPERTIES OF MAXIMUM LIKELIHOOD ESTIMATORS IN MULTINOMIAL PROBIT MODELS
• THE ESTIMATION OF DISCRETE CHOICE MODELS WITH LARGE CHOICE SET

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