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Marginal Effects in Multivariate Probit and Kindred Discrete and Count Outcome Models, with Applications in Health Economics

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  • John Mullahy

Abstract

Estimation of marginal or partial effects of covariates x on various conditional parameters or functionals is often the main target of applied microeconometric analysis. In the specific context of probit models such estimation is straightforward in univariate models, and Greene, 1996, 1998, has extended these results to cover the case of quadrant probability marginal effects in bivariate probit models. The purpose of this paper is to extend these results to the general multivariate probit context for arbitrary orthant probabilities and to demonstrate the applicability of such extensions in contexts of interest in health economics applications. The baseline results are extended to models that condition on subvectors of y, to count data structures that derive from the probability structure of y, to multivariate ordered probit data structures, and to multinomial probit models whose marginal effects turn out to be a special case of those of the multivariate probit model. Simulations reveal that analytical formulae versus fully numerical derivatives result in a reduction in computational time as well as an increase in accuracy.

Suggested Citation

  • John Mullahy, 2011. "Marginal Effects in Multivariate Probit and Kindred Discrete and Count Outcome Models, with Applications in Health Economics," NBER Working Papers 17588, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:17588
    Note: HC HE TWP
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    1. Fichera, Eleonora & Sutton, Matt, 2011. "State and self investments in health," Journal of Health Economics, Elsevier, vol. 30(6), pages 1164-1173.
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    Cited by:

    1. Lorenzo Almada & Ian McCarthy, 2017. "It's a Cruel Summer: Household Responses to Reductions in Government Nutrition Assistance," NBER Working Papers 23633, National Bureau of Economic Research, Inc.
    2. Us, Anna & Florkowski, Wojciech J. & Klepacka, Anna M., 5. "From Water To Biofuels: Knowledge And Attitudes Towards Renewable Energy Sources Among Rural Residents In Eastern Poland," Roczniki (Annals), Polish Association of Agricultural Economists and Agribusiness - Stowarzyszenie Ekonomistow Rolnictwa e Agrobiznesu (SERiA), issue 5.
    3. Dang,Hai-Anh H. & Lanjouw,Peter F., 2013. "Measuring poverty dynamics with synthetic panels based on cross-sections," Policy Research Working Paper Series 6504, The World Bank.
    4. Kim, Changjoo & Parent, Olivier, 2016. "Modeling individual travel behaviors based on intra-household interactions," Regional Science and Urban Economics, Elsevier, vol. 57(C), pages 1-11.
    5. Angioloni, Simone & Kudabaev, Zarylbek & Ames, Glenn & Wetzstein, Michael, 2015. "Household Allocation of Microfinance Loans in Kyrgyzstan," 2015 Conference, August 9-14, 2015, Milan, Italy 210949, International Association of Agricultural Economists.
    6. Hayo Bernd & Caris Tobias, 2013. "Female Labour Force Participation in the MENA Region: The Role of Identity," Review of Middle East Economics and Finance, De Gruyter, vol. 9(3), pages 271-292, December.
    7. Fu, Shengfei & Shonkwiler, John Scott, 2015. "A New Estimator for Multivariate Binary Data," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 204963, Agricultural and Applied Economics Association;Western Agricultural Economics Association.

    More about this item

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • I1 - Health, Education, and Welfare - - Health

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