Estimation of sample selection models with two selection mechanisms
AbstractThis paper focuses on estimating sample selection models with two incidentally truncated outcomes and two corresponding selection mechanisms. The method of estimation is an extension of the Markov chain Monte Carlo (MCMC) sampling algorithm from Chib (2007) and Chib etÂ al. (2009). Contrary to conventional data augmentation strategies when dealing with missing data, the proposed algorithm augments the posterior with only a small subset of the total missing data caused by sample selection. This results in improved convergence of the MCMC chain and decreased storage costs, while maintaining tractability in the sampling densities. The methods are applied to estimate the effects of residential density on vehicle miles traveled and vehicle holdings in California.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 55 (2011)
Issue (Month): 2 (February)
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Web page: http://www.elsevier.com/locate/csda
Sample selection Markov chain Monte Carlo Data augmentation;
Other versions of this item:
- 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.
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