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Estimation of multinomial probit-kernel integrated choice and latent variable model: comparison on one sequential and two simultaneous approaches

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Abstract

Integrated choice and latent variable (ICLV) model incorporates latent factors into standard discrete choice model with aim to provide greater explanatory power. Using simulated datasets, this study makes a comparison among three estimation approaches corresponding to the sequential approach and two simultaneous approaches including the maximum simulated likelihood with GHK estimator and maximum approximate composite marginal likelihood (MACML) approach, to evaluate their abilities to recover the underlying parameters of multinomial probit-kernel ICLV model. The results show that both simultaneous approaches outperform the sequential approach in terms of estimates accuracy and efficiency irrespective of the sample sizes, and the MACML approach is the most preferable due to its best performance on recovering true values of parameters with relatively small standard errors, especially when the sample size is large enough.

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Acknowledgments

This research is sponsored by the National Natural Science Foundation of China (No.51278301). The authors would like to express their appreciation to anyone who has provided suggestions and comments on this paper.

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  1. Antai College of Economics & Management, Shanghai Jiao Tong University, Room 202 South Building, No. 535 Fahuazhen Road, Changning District, Shanghai, China

    Xuemei Fu & Zhicai Juan

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  1. Xuemei Fu
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  2. Zhicai Juan
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Correspondence to Xuemei Fu.

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Fu, X., Juan, Z. Estimation of multinomial probit-kernel integrated choice and latent variable model: comparison on one sequential and two simultaneous approaches. Transportation 44, 91–116 (2017). https://doi.org/10.1007/s11116-015-9626-x

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