On consistency of the MACML approach to discrete choice modelling
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DOI: 10.1016/j.jocm.2018.10.001
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- Carlos F. Daganzo & Fernando Bouthelier & Yosef Sheffi, 1977. "Multinomial Probit and Qualitative Choice: A Computationally Efficient Algorithm," Transportation Science, INFORMS, vol. 11(4), pages 338-358, November.
- Ennio Cascetta, 2009. "Transportation Systems Analysis," Springer Optimization and Its Applications, Springer, number 978-0-387-75857-2, June.
- Matzkin, Rosa L., 1993. "Nonparametric identification and estimation of polychotomous choice models," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 137-168, July.
- Train,Kenneth E., 2009.
"Discrete Choice Methods with Simulation,"
Cambridge Books,
Cambridge University Press, number 9780521766555.
- Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, September.
- Kenneth Train, 2003. "Discrete Choice Methods with Simulation," Online economics textbooks, SUNY-Oswego, Department of Economics, number emetr2.
- Wagner A. Kamakura, 1989. "The Estimation of Multinomial Probit Models: A New Calibration Algorithm," Transportation Science, INFORMS, vol. 23(4), pages 253-265, November.
- Bhat, Chandra R., 2011. "The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 923-939, August.
- Xuemei Fu & Zhicai Juan, 2017. "Estimation of multinomial probit-kernel integrated choice and latent variable model: comparison on one sequential and two simultaneous approaches," Transportation, Springer, vol. 44(1), pages 91-116, January.
- Liu, Yu-Hsin & Mahmassani, Hani S., 2000. "Global maximum likelihood estimation procedure for multinomial probit (MNP) model parameters," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 419-449, June.
- Andriy Norets & Satoru Takahashi, 2013. "On the surjectivity of the mapping between utilities and choice probabilities," Quantitative Economics, Econometric Society, vol. 4(1), pages 149-155, March.
- Lee, Lung-Fei, 1992.
"On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models,"
Econometric Theory, Cambridge University Press, vol. 8(4), pages 518-552, December.
- Lee, L-F., 1990. "On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models," Papers 260, Minnesota - Center for Economic Research.
- Lewandowski, Daniel & Kurowicka, Dorota & Joe, Harry, 2009. "Generating random correlation matrices based on vines and extended onion method," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1989-2001, October.
- Joel L. Horowitz & Jürg M. Sparmann & Carlos F. Daganzo, 1982. "An Investigation of the Accuracy of the Clark Approximation for the Multinomial Probit Model," Transportation Science, INFORMS, vol. 16(3), pages 382-401, August.
- Bhat, Chandra R. & Sidharthan, Raghuprasad, 2011. "A simulation evaluation of the maximum approximate composite marginal likelihood (MACML) estimator for mixed multinomial probit models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 940-953, August.
- Martinetti, Davide & Geniaux, Ghislain, 2017. "Approximate likelihood estimation of spatial probit models," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 30-45.
- Patil, Priyadarshan N. & Dubey, Subodh K. & Pinjari, Abdul R. & Cherchi, Elisabetta & Daziano, Ricardo & Bhat, Chandra R., 2017. "Simulation evaluation of emerging estimation techniques for multinomial probit models," Journal of choice modelling, Elsevier, vol. 23(C), pages 9-20.
- Michael G. Langdon, 1984. "Improved Algorithms for Estimating Choice Probabilities in the Multinomial Probit Model," Transportation Science, INFORMS, vol. 18(3), pages 267-299, August.
- Bolduc, Denis, 1999. "A practical technique to estimate multinomial probit models in transportation," Transportation Research Part B: Methodological, Elsevier, vol. 33(1), pages 63-79, February.
- Bhat, Chandra R., 2018. "New matrix-based methods for the analytic evaluation of the multivariate cumulative normal distribution function," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 238-256.
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Keywords
Multinomial probit; Discrete choice model; MACML estimation approach; Estimation method; Monte Carlo experiment;All these keywords.
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