### Abstract

Language | English |
---|---|

Place of Publication | Glasgow |

Publisher | University of Strathclyde |

Pages | 1-48 |

Number of pages | 49 |

Volume | 11 |

Publication status | Published - Jan 2011 |

### Fingerprint

### Keywords

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

### Cite this

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

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**Bayesian Model Averaging in the Instrumental Variable Regression Model.** / Koop, Gary; Leon-Gonzalez, Roberto; Strachan, Rodney.

Research output: Working paper › Discussion paper

TY - UNPB

T1 - Bayesian Model Averaging in the Instrumental Variable Regression Model

AU - Koop, Gary

AU - Leon-Gonzalez, Roberto

AU - Strachan, Rodney

N1 - Discussion paper.

PY - 2011/1

Y1 - 2011/1

N2 - 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.

AB - 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.

KW - bayesian

KW - endogeneity

KW - simultaneous equations

KW - reversible jump Markov chain Monte Carlo

M3 - Discussion paper

VL - 11

SP - 1

EP - 48

BT - Bayesian Model Averaging in the Instrumental Variable Regression Model

PB - University of Strathclyde

CY - Glasgow

ER -