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MCMC Approach to Classical Estimation with Overidentifying Restrictions

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  • Luis Quintero

Abstract

We extend the Laplace estimators approach proposed by Chernozhukov and Hong (2003) by incorporating information in the space of overidentifying re- strictions (OR) in GMM, information previously ignored during parameter es- timation in Bayesian methods. Parameters and test statistics are estimated simultaneously using the entire equation domain, not only the global mini- mum. Markov Chain Monte Carlo avoids the curse of dimensionality while kernel density estimation allows estimators that condition on OR being satis- fied. This method uses economic theory as criteria for estimate selection when facing multiplicity. Our estimators outperform counterparts in simulation of an asset pricing model in Hall and Horowitz (1996).

Suggested Citation

  • Luis Quintero, "undated". "MCMC Approach to Classical Estimation with Overidentifying Restrictions," GSIA Working Papers 2013-E13, Carnegie Mellon University, Tepper School of Business.
    • Handle: RePEc:cmu:gsiawp:473528242
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    References listed on IDEAS

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