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Bayesian geoadditive sample selection models

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  • Manuel Wiesenfarth
  • Thomas Kneib

Abstract

Summary. 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.

Suggested Citation

  • 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, May.
  • Handle: RePEc:bla:jorssc:v:59:y:2010:i:3:p:381-404
    DOI: 10.1111/j.1467-9876.2009.00698.x
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    References listed on IDEAS

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    Cited by:

    1. Wiemann, Paul F.V. & Klein, Nadja & Kneib, Thomas, 2022. "Correcting for sample selection bias in Bayesian distributional regression models," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    2. 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.
    3. 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.
    4. Eichenauer, Vera Z. & Fuchs, Andreas & Kunze, Sven & Strobl, Eric, 2020. "Distortions in aid allocation of United Nations flash appeals: Evidence from the 2015 Nepal earthquake," World Development, Elsevier, vol. 136(C).
    5. Mogge, Lukas & McDonald, Morag & Knoth, Christian & Teickner, Henning & Purevtseren, Myagmartseren & Pebesma, Edzer & Kraehnert, Kati, 2023. "Allocation of humanitarian aid after a weather disaster," World Development, Elsevier, vol. 166(C).
    6. Fuchs, Andreas & Klann, Nils-Hendrik, 2013. "Emergency Aid 2.0," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79898, Verein für Socialpolitik / German Economic Association.
    7. Wojtyś, Magorzata & Marra, Giampiero & Radice, Rosalba, 2016. "Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i06).
    8. Marra, Giampiero & Radice, Rosalba, 2013. "Estimation of a regression spline sample selection model," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 158-173.
    9. Karol Wyszynski & Giampiero Marra, 2018. "Sample selection models for count data in R," Computational Statistics, Springer, vol. 33(3), pages 1385-1412, September.
    10. Zhao, Jun & Kim, Hea-Jung & Kim, Hyoung-Moon, 2020. "New EM-type algorithms for the Heckman selection model," Computational Statistics & Data Analysis, Elsevier, vol. 146(C).

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