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Bayesian geoadditive sample selection models
Author(s) -
Wiesenfarth Manuel,
Kneib Thomas
Publication year - 2010
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2009.00698.x
Subject(s) - structural equation modeling , covariate , markov chain monte carlo , outcome (game theory) , bayesian inference , econometrics , computer science , model selection , sample (material) , bayesian probability , selection (genetic algorithm) , monte carlo method , statistics , mathematics , machine learning , mathematical economics , chromatography , chemistry
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.