Projects per year
Abstract
This paper considers the instrumental variable regression model when there is uncertainty about the set of instruments, exogeneity restrictions, the validity of identifying restrictions and the set of exogenous regressors. This uncertainty can result in a huge number of models. To avoid statistical problems associated with standard model selection procedures, we develop a reversible jump Markov chain Monte Carlo algorithm that allows us to do Bayesian model averaging. The algorithm is very flexible and can be easily adapted to analyze any of the different priors that have been proposed in the Bayesian instrumental variables literature. We show how to calculate the probability of any relevant restriction (e.g. the posterior probability that overidentifying restrictions hold) and discuss diagnostic checking using the posterior distribution of discrepancy vectors. We illustrate our methods in a returnstoschooling application.
Original language  English 

Place of Publication  Glasgow 
Publisher  University of Strathclyde 
Pages  148 
Number of pages  49 
Volume  11 
Publication status  Published  Jan 2011 
Keywords
 bayesian
 endogeneity
 simultaneous equations
 reversible jump Markov chain Monte Carlo
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Projects
 1 Finished

Collaborating to attach goods and detach bads: How actors collaborate in marketing green chemistry
Finch, J.
7/09/09 → 30/09/13
Project: Research