Abstract
We develop a Bayesian semi-parametric approach to the instrumental variable problem. We assume linear structural and reduced form equations, but model the error distributions non-parametrically. A Dirichlet process prior is used for the joint distribution of structural and instrumental variable equations errors. Our implementation of the Dirichlet process prior uses a normal distribution as a base model. It can therefore be interpreted as modeling the unknown joint distribution with a mixture of normal distributions with a variable number of mixture components. We demonstrate that this procedure is both feasible and sensible using actual and simulated data. Sampling experiments compare inferences from the non-parametric Bayesian procedure with those based on procedures from the recent literature on weak instrument asymptotics. When errors are non-normal, our procedure is more efficient than standard Bayesian or classical methods.
Original language | English (US) |
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Pages (from-to) | 276-305 |
Number of pages | 30 |
Journal | Journal of Econometrics |
Volume | 144 |
Issue number | 1 |
DOIs | |
State | Published - May 1 2008 |
Externally published | Yes |
Keywords
- Dirichlet process priors
- Instrumental variables
- Semi-parametric Bayesian inference
ASJC Scopus subject areas
- Economics and Econometrics