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Estimation of Sample Selection Models With Two Selection Mechanisms

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  • Li, Phillip

Abstract

This paper focuses on estimating limited dependent variable models with incidentally truncated data and two selection mechanisms. While typical sample selection models have been widely estimated, extensions to multiple selection mechanisms have been sparse due to intractable likelihood functions or estimation algorithms with slow convergence. This paper extends the sampling algorithm from Chib et al. (2009) and proposes a computationally-ecient Markov chain Monte Carlo (MCMC) estimation algorithm with data augmentation. The algorithm only augments the posterior with a small subset of the total missing data caused by the selection mechanisms, which improves convergence of the MCMC chain and decreases computational load relative to standard algorithms. The resulting sampling densities are well-known despite not having the "complete" data. The methods are applied to estimate the e�ects of residential density on vehicle usage and holdings in California.

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  • Li, Phillip, 2010. "Estimation of Sample Selection Models With Two Selection Mechanisms," University of California Transportation Center, Working Papers qt0h97w9x2, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt0h97w9x2
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    1. Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2007. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9780521671736, May.
    2. Charles F. Manski, 1989. "Anatomy of the Selection Problem," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 343-360.
    3. Chib, Siddhartha, 2001. "Markov chain Monte Carlo methods: computation and inference," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 57, pages 3569-3649 Elsevier.
    4. E. Roy Weintraub & Evelyn L. Forget, 2007. "Introduction," History of Political Economy, Duke University Press, vol. 39(5), pages 1-6, Supplemen.
    5. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters,in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492 National Bureau of Economic Research, Inc.
    6. J. Scott Shonkwiler & Steven T. Yen, 1999. "Two-Step Estimation of a Censored System of Equations," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(4), pages 972-982.
    7. Newey, Whitney K & Powell, James L & Walker, James R, 1990. "Semiparametric Estimation of Selection Models: Some Empirical Results," American Economic Review, American Economic Association, vol. 80(2), pages 324-328, May.
    8. Antonio M. Bento & Maureen L. Cropper & Ahmed Mushfiq Mobarak & Katja Vinha, 2005. "The Effects of Urban Spatial Structure on Travel Demand in the United States," The Review of Economics and Statistics, MIT Press, vol. 87(3), pages 466-478, August.
    9. Puhani, Patrick A, 2000. " The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
    10. Chib, Siddhartha, 2007. "Analysis of treatment response data without the joint distribution of potential outcomes," Journal of Econometrics, Elsevier, vol. 140(2), pages 401-412, October.
    11. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    12. Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August.
    13. Steven T. Yen, 2005. "A Multivariate Sample-Selection Model: Estimating Cigarette and Alcohol Demands with Zero Observations," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(2), pages 453-466.
    14. van Hasselt, Martijn, 2011. "Bayesian inference in a sample selection model," Journal of Econometrics, Elsevier, vol. 165(2), pages 221-232.
    15. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-318, May.
    16. Poirier, Dale J., 1980. "Partial observability in bivariate probit models," Journal of Econometrics, Elsevier, vol. 12(2), pages 209-217, February.
    17. Amemiya, Takeshi, 1984. "Tobit models: A survey," Journal of Econometrics, Elsevier, vol. 24(1-2), pages 3-61.
    18. Brownstone, David & Golob, Thomas F., 2009. "The impact of residential density on vehicle usage and energy consumption," Journal of Urban Economics, Elsevier, vol. 65(1), pages 91-98, January.
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    Cited by:

    1. Angela Vossmeyer, 2014. "Treatment Effects and Informative Missingness with an Application to Bank Recapitalization Programs," American Economic Review, American Economic Association, vol. 104(5), pages 212-217, May.
    2. Marra, Giampiero & Wyszynski, Karol, 2016. "Semi-parametric copula sample selection models for count responses," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 110-129.
    3. Marra, Giampiero & Radice, Rosalba, 2013. "Estimation of a regression spline sample selection model," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 158-173.
    4. Biørn, Erik & Wangen, Knut R., 2012. "New Taxonomies for Limited Dependent Variables Models," MPRA Paper 41461, University Library of Munich, Germany.

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