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Estimation of multivariate probit models via bivariate probit

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  • John Mullahy

    (University of Wisconsin–Madison)

Abstract

In this article, I suggest the utility of fitting multivariate probit models using a chain of bivariate probit estimators. This approach is based on Stata’s biprobit and suest commands and is driven by a Mata function, bvpmvp(). I discuss two potential advantages of the approach over the mvprobit command (Cappellari and Jenkins, 2003, Stata Journal 3: 278–294): significant reductions in computation time and essentially unlimited dimensionality of the outcome set. Computation time is reduced because the approach does not rely on simulation methods; unlimited dimensionality arises because only pairs of outcomes are con- sidered at each estimation stage. This approach provides a consistent estimator of all the multivariate probit model’s parameters under the same assumptions re- quired for consistent estimation via mvprobit, and simulation exercises I provide suggest no loss of estimator precision relative to mvprobit. Copyright 2016 by StataCorp LP.

Suggested Citation

  • John Mullahy, 2016. "Estimation of multivariate probit models via bivariate probit," Stata Journal, StataCorp LP, vol. 16(1), pages 37-51, March.
    • Handle: RePEc:tsj:stataj:v:16:y:2016:i:1:p:37-51
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    Citations

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    Cited by:

    1. Nathan Kettlewell, 2019. "Utilization and Selection in an Ancillaries Health Insurance Market," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 86(4), pages 989-1017, December.
    2. Miller, Ray & Bairoliya, Neha & Canning, David, 2019. "Health disparities and the socioeconomic gradient in elderly life-cycle consumption," The Journal of the Economics of Ageing, Elsevier, vol. 14(C).
    3. Qiu, Lingling & Kant, Shashi & Zeng, Weizhong, 2023. "Indigenous people's perceptions of benefits and costs of China's second phase of the grain for green program and the influencing factors," Ecological Economics, Elsevier, vol. 214(C).
    4. Florence Barugahara & Mildred Barungi, 2023. "Characteristics, Determinants, Challenges and Performance of Self-employment among the Youth in Uganda," International Journal of Economics and Financial Issues, Econjournals, vol. 13(3), pages 1-10, May.
    5. Millimet, Daniel L. & Bellemare, Marc, 2023. "Fixed Effects and Causal Inference," IZA Discussion Papers 16202, Institute of Labor Economics (IZA).
    6. Navarro-Castañeda, Sandro & Arranz, José M. & Burguillo, Mercedes & Colla De Robertis, Esteban, 2021. "Land tenure security and agrarian investments in the Peruvian Highlands," Land Use Policy, Elsevier, vol. 109(C).
    7. Seth Ooko Onyango & Sabina Mukoya-Wangia & Josiah Mwivandi Kinama & Pamela Akinyi Olet, 2024. "Integration of Tsetse Fly and Trypanosomiasis Control Methods From Livestock Farmers’ Perspective: A Multivariate Probit Approach," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 12(10), pages 1-67, April.
    8. Bryan Ting & Fred Wright & Yi-Hui Zhou, 2022. "Fast Multivariate Probit Estimation via a Two-Stage Composite Likelihood," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 533-549, December.
    9. John Mullahy, 2017. "Marginal effects in multivariate probit models," Empirical Economics, Springer, vol. 52(2), pages 447-461, March.

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