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Bayesian Model Averaging in the Instrumental Variable Regression Model

Author

Listed:
  • Gary Koop

    (Department of Economics, University of Strathclyde)

  • Roberto Leon-Gonzalez

    (National Graduate Institute for Policy Studies)

  • Rodney Strachan

    (The Australian National University)

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 ?exible and can be easily adapted to analyze any of the di¤erent 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.

Suggested Citation

  • Gary Koop & Roberto Leon-Gonzalez & Rodney Strachan, 2011. "Bayesian Model Averaging in the Instrumental Variable Regression Model," Working Papers 1112, University of Strathclyde Business School, Department of Economics.
    • Handle: RePEc:str:wpaper:1112
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Bayesian; endogeneity; simultaneous equations; reversible jump Markov chain Monte Carlo.;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

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