Bayesian Factor Model Shrinkage for Linear IV Regression With Many Instruments

P. Richard Hahn, Jingyu He and Hedibert Lopes

Journal of Business & Economic Statistics, 2018, vol. 36, issue 2, 278-287

Abstract: A Bayesian approach for the many instruments problem in linear instrumental variable models is presented. The new approach has two components. First, a slice sampler is developed, which leverages a decomposition of the likelihood function that is a Bayesian analogue to two-stage least squares. The new sampler permits nonconjugate shrinkage priors to be implemented easily and efficiently. The new computational approach permits a Bayesian analysis of problems that were previously infeasible due to computational demands that scaled poorly in the number of regressors. Second, a new predictor-dependent shrinkage prior is developed specifically for the many instruments setting. The prior is constructed based on a factor model decomposition of the matrix of observed instruments, allowing many instruments to be incorporated into the analysis in a robust way. Features of the new method are illustrated via a simulation study and three empirical examples.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:36:y:2018:i:2:p:278-287

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DOI: 10.1080/07350015.2016.1172968

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