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A limited information estimator for the multivariate ordinal probit model

Author

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  • Tsu-Tan Fu
  • Lung-An Li
  • Yih-Ming Lin
  • Kamhon Kan

Abstract

A limited information estimator for the multivariate ordinal probit model is developed. The main advantage of the estimator is that even for high dimensional models, the estimation procedure requires the evaluation of bivariate normal integrals only. The proposed estimator also avoids the potential problem of encountering local maxima in the estimation process, which is looming using maximum likelihood. The performance of the limited information estimator is shown by Monte Carlo experiments to be excellent and it is comparable to that of the maximum likelihood estimator. Finally, an application of the limited information multivariate ordinal probit to model the consumption level of cigarette, alcohol and betel nut is presented.

Suggested Citation

  • Tsu-Tan Fu & Lung-An Li & Yih-Ming Lin & Kamhon Kan, 2000. "A limited information estimator for the multivariate ordinal probit model," Applied Economics, Taylor & Francis Journals, vol. 32(14), pages 1841-1851.
    • Handle: RePEc:taf:applec:v:32:y:2000:i:14:p:1841-1851
    DOI: 10.1080/000368400425062
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    References listed on IDEAS

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    1. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    2. Tsu-tan Fu & James E. Epperson & Joseph V. Terza & Stanley M. Fletcher, 1988. "Producer Attitudes Toward Peanut Market Alternatives: An Application of Multivariate Probit Joint Estimation," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 70(4), pages 910-918.
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    Cited by:

    1. Li, Yonghai & Schafer, Daniel W., 2008. "Likelihood analysis of the multivariate ordinal probit regression model for repeated ordinal responses," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3474-3492, March.
    2. Auld, Joshua & Mohammadian, Abolfazl(Kouros), 2012. "Activity planning processes in the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1386-1403.
    3. Liu, Feng & Zhao, Shaoqiong & Li, Yang, 2017. "How many, how often, and how new? A multivariate profiling of mobile app users," Journal of Retailing and Consumer Services, Elsevier, vol. 38(C), pages 71-80.

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