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MNP: R Package for Fitting the Multinomial Probit Model

Author

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  • Imai, Kosuke
  • Van Dyk, David A.

Abstract

MNP is a publicly available R package that fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP software can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005).

Suggested Citation

  • Imai, Kosuke & Van Dyk, David A., 2005. "MNP: R Package for Fitting the Multinomial Probit Model," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i03).
    • Handle: RePEc:jss:jstsof:v:014:i03
    DOI: http://hdl.handle.net/10.18637/jss.v014.i03
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    Citations

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    Cited by:

    1. Rub'en Loaiza-Maya & Didier Nibbering, 2022. "Fast variational Bayes methods for multinomial probit models," Papers 2202.12495, arXiv.org, revised Oct 2022.
    2. Michael O'Kelly & John Doyle & Philip J. Boland, 2010. "How many ways can you look at a proportion?: cross‐community vote transfers in Northern Ireland after the Belfast Agreement," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 215-235, January.
    3. Raja Chakir & Olivier Parent, 2009. "Determinants of land use changes: A spatial multinomial probit approach," Papers in Regional Science, Wiley Blackwell, vol. 88(2), pages 327-344, June.
    4. Zhang, Xiao & Boscardin, W. John & Belin, Thomas R., 2008. "Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3697-3708, March.
    5. Ruben Loaiza-Maya & Didier Nibbering, 2020. "Scalable Bayesian Estimation in the Multinomial Probit Model," Monash Econometrics and Business Statistics Working Papers 25/20, Monash University, Department of Econometrics and Business Statistics.
    6. Yiyi Wang & Kara Kockelman & Paul Damien, 2014. "A spatial autoregressive multinomial probit model for anticipating land-use change in Austin, Texas," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(1), pages 251-278, January.
    7. Fabio Blasutto & Egor Kozlov, 2020. "(Changing) Marriage and Cohabitation Patterns in the US: do Divorce Laws Matter?," 2020 Papers pbl245, Job Market Papers.
    8. repec:jss:jstsof:14:i03 is not listed on IDEAS
    9. Seongkyoon Jeong & Jae Young Choi & Jaeyun Kim, 2011. "The determinants of research collaboration modes: exploring the effects of research and researcher characteristics on co-authorship," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 967-983, December.

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