Estimation of sample selection models with two selection mechanisms

Phillip Li ()

Computational Statistics & Data Analysis, 2011, vol. 55, issue 2, 1099-1108

Abstract: This 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 et 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.

Keywords: Sample; selection; Markov; chain; Monte; Carlo; Data; augmentation (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Related works:
Working Paper: Estimation of Sample Selection Models With Two Selection Mechanisms (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:2:p:1099-1108

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