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.
Original language | English (US) |
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Pages (from-to) | 278-287 |
Number of pages | 10 |
Journal | Journal of Business and Economic Statistics |
Volume | 36 |
Issue number | 2 |
DOIs | |
State | Published - Apr 3 2018 |
Externally published | Yes |
Keywords
- Bayesian econometrics
- Horseshoe prior
- Instrumental variables
- Slice sampler
ASJC Scopus subject areas
- Statistics and Probability
- Social Sciences (miscellaneous)
- Economics and Econometrics
- Statistics, Probability and Uncertainty