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FIML estimation of treatment effect models with endogenous selection and multiple censored responses via a Monte Carlo EM Algorithm

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  • Ricardo Smith Ramírez

    (Division of Economics, CIDE)

Abstract

We formulate a Monte Carlo EM algorithm to estimate treatment effect models involving multiple censored responses. The algorithm has at least three advantages with respect to traditional methods. First, it does not require integrating the unobserved information out from the likelihood function, which reduces the estimation time dramatically and permits to solve problems involving a high number of latent variables. Second, it reduces the estimation of the vector of slopes to the calculation of a GLS estimator, and numerical techniques are required only to estimate the elements in the disturbance covariance matrix. Third, it has low sensitivity to the selection of starting values and fragile identification. We illustrate the method by estimating a 3-equation treatment model; then we compare the performance of our algorithm against a quasi-Newton optimization that uses numerical integration.

Suggested Citation

  • Ricardo Smith Ramírez, 2007. "FIML estimation of treatment effect models with endogenous selection and multiple censored responses via a Monte Carlo EM Algorithm," Working papers DTE 403, CIDE, División de Economía.
    • Handle: RePEc:emc:wpaper:dte403
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    References listed on IDEAS

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    1. Geweke, John & Keane, Michael P & Runkle, David, 1994. "Alternative Computational Approaches to Inference in the Multinomial Probit Model," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 609-632, November.
    2. 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.
    3. Borsch-Supan, Axel & Hajivassiliou, Vassilis A., 1993. "Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models," Journal of Econometrics, Elsevier, vol. 58(3), pages 347-368, August.
    4. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
    5. Eickhoff, Jens C. & Zhu, Jun & Amemiya, Yasuo, 2004. "On the simulation size and the convergence of the Monte Carlo EM algorithm via likelihood-based distances," Statistics & Probability Letters, Elsevier, vol. 67(2), pages 161-171, April.
    6. Joseph C. Cooper, 2003. "A Joint Framework for Analysis of Agri-Environmental Payment Programs," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(4), pages 976-987.
    7. Khanna, Madhu & Isik, Murat & Zilberman, David, 2002. "Cost-effectiveness of alternative green payment policies for conservation technology adoption with heterogeneous land quality," Agricultural Economics, Blackwell, vol. 27(2), pages 157-174, August.
    8. Keane, Michael P, 1992. "A Note on Identification in the Multinomial Probit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 193-200, April.
    9. Sobol, I.M., 1998. "On quasi-Monte Carlo integrations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 47(2), pages 103-112.
    10. Natarajan, Ranjini & McCulloch, Charles E. & Kiefer, Nicholas M., 2000. "A Monte Carlo EM method for estimating multinomial probit models," Computational Statistics & Data Analysis, Elsevier, vol. 34(1), pages 33-50, July.
    11. Jeffrey H. Dorfman, 1996. "Modeling Multiple Adoption Decisions in a Joint Framework," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(3), pages 547-557.
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    More about this item

    Keywords

    FIML estimation; treatment effect models; endogenous selection; multiple censored responses; Monte Carlo EM Algorithm;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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