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Bayesian geoadditive sample selection models

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  • Manuel Wiesenfarth
  • Thomas Kneib

Abstract

Sample selection models attempt to correct for non-randomly selected data in a two-model hierarchy where, on the first level, a binary selection equation determines whether a particular observation will be available for the second level, i.e. in the outcome equation. Ignoring the non-random selection mechanism that is induced by the selection equation may result in biased estimation of the coefficients in the outcome equation. In the application that motivated this research, we analyse relief supply in earthquake-affected communities in Pakistan, where the decision to deliver goods represents the dependent variable in the selection equation whereas factors that determine the amount of goods supplied are analysed in the outcome equation. In this application, the inclusion of spatial effects is necessary since the available covariate information on the community level is rather scarce. Moreover, the high temporal dynamics underlying the immediate delivery of relief supply after a natural disaster calls for non-linear, time varying effects. We propose a geoadditive sample selection model that allows us to address these issues in a general Bayesian framework with inference being based on Markov chain Monte Carlo simulation techniques. The model proposed is studied in simulations and applied to the relief supply data from Pakistan. Copyright (c) 2010 Royal Statistical Society.

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  • Manuel Wiesenfarth & Thomas Kneib, 2010. "Bayesian geoadditive sample selection models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(3), pages 381-404.
  • Handle: RePEc:bla:jorssc:v:59:y:2010:i:3:p:381-404
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    References listed on IDEAS

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    1. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    2. Sigelman, Lee & Zeng, Langche, 1999. "Analyzing Censored and Sample-Selected Data with Tobit and Heckit Models," Political Analysis, Cambridge University Press, vol. 8(02), pages 167-182, December.
    3. Omori, Yasuhiro, 2007. "Efficient Gibbs sampler for Bayesian analysis of a sample selection model," Statistics & Probability Letters, Elsevier, vol. 77(12), pages 1300-1311, July.
    4. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
    5. Mitali Das & Whitney K. Newey & Francis Vella, 2003. "Nonparametric Estimation of Sample Selection Models," Review of Economic Studies, Oxford University Press, vol. 70(1), pages 33-58.
    6. Kai, Li, 1998. "Bayesian inference in a simultaneous equation model with limited dependent variables," Journal of Econometrics, Elsevier, vol. 85(2), pages 387-400, August.
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    Cited by:

    1. Manuel Wiesenfarth & Carlos Matías Hisgen & Thomas Kneib & Carmen Cadarso-Suarez, 2014. "Bayesian Nonparametric Instrumental Variables Regression Based on Penalized Splines and Dirichlet Process Mixtures," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 468-482, July.
    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. Fuchs, Andreas & Klann, Nils-Hendrik, 2013. "Emergency Aid 2.0," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79898, Verein für Socialpolitik / German Economic Association.
    4. Marra, Giampiero & Radice, Rosalba, 2013. "Estimation of a regression spline sample selection model," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 158-173.

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