55 Pages Posted: 15 Jul 2003
Date Written: December 2002
This paper studies computationally and theoretically attractive estimators referred here as to the Laplace type estimators (LTE). The LTE include means and quantiles of Quasi-posterior distributions defined as transformations of general
(non-likelihood-based) statistical criterion functions, such as those in GMM, nonlinear IV, empirical likelihood, and minimum distance methods. The approach generates an alternative to classical extremum estimation and also falls outside the parametric Bayesian approach. For example, it offers a new attractive estimation method for such important semi-parametric problems as censored and instrumental quantile regression, nonlinear IV, GMM, and value-at-risk, models. The LTE's are computed using Markov Chain Monte Carlo methods, which help circumvent the computational curse of dimensionality. A large sample theory is obtained and illustrated for regular cases.
Keywords: Laplace, Bayes, Markov Chain Monte Carlo, GMM, Instrumental Regression, Censored Quantile Regression, Instrumental Quantile Regression, Empirical Likelihood, Value-at-Risk
JEL Classification: C10, C11, C13, C15
Suggested Citation: Suggested Citation
Chernozhukov, Victor and Hong, Han, An MCMC Approach to Classical Estimation (December 2002). MIT Department of Economics Working Paper No. 03-21. Available at SSRN: https://ssrn.com/abstract=420371 or http://dx.doi.org/10.2139/ssrn.420371