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Maximum simulated likelihood estimation of a negative binomial regression model with multinomial endogenous treatment

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  • Partha Deb

    () (Hunter College, City University of New York)

  • Pravin K. Trivedi

    () (Indiana University)

Abstract

We describe specification and estimation of a multinomial treatment effects negative binomial regression model. A latent factor structure is used to accommodate selection into treatment, and a simulated likelihood method is used for estimation. We describe its implementation via the mtreatnb command. Copyright 2006 by StataCorp LP.

Suggested Citation

  • Partha Deb & Pravin K. Trivedi, 2006. "Maximum simulated likelihood estimation of a negative binomial regression model with multinomial endogenous treatment," Stata Journal, StataCorp LP, vol. 6(2), pages 246-255, June.
  • Handle: RePEc:tsj:stataj:v:6:y:2006:i:2:p:246-255
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    References listed on IDEAS

    as
    1. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    2. Lee, Lung-Fei, 1983. "Generalized Econometric Models with Selectivity," Econometrica, Econometric Society, vol. 51(2), pages 507-512, March.
    3. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
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    22. Michael J. Peel, 2014. "Addressing unobserved endogeneity bias in accounting studies: control and sensitivity methods by variable type," Accounting and Business Research, Taylor & Francis Journals, vol. 44(5), pages 545-571, October.
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