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 over-identifying restrictions hold) and discuss diagnostic checking using the posterior distribution of discrepancy vectors. We illustrate our methods in a returns-to-schooling application.

Original language | English |
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Place of Publication | Glasgow |

Publisher | University of Strathclyde |

Pages | 1-48 |

Number of pages | 49 |

Volume | 11 |

Publication status | Published - Jan 2011 |

### Keywords

- bayesian
- endogeneity
- simultaneous equations
- reversible jump Markov chain Monte Carlo

## Fingerprint Dive into the research topics of 'Bayesian Model Averaging in the Instrumental Variable Regression Model'. Together they form a unique fingerprint.

## 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

## Cite this

Koop, G., Leon-Gonzalez, R., & Strachan, R. (2011).

*Bayesian Model Averaging in the Instrumental Variable Regression Model*. (12 ed.) (pp. 1-48). University of Strathclyde.