An MCMC Approach to Classical Estimation

Authors

Victor Chernozhukov, Han Hong

 

Department of Economics, Massachusetts Institute of Technology, Cambridge, MA 02142, USA

 

Department of Economics, Princeton

University, Princeton, NJ 08544, USA}

 

Forthcoming in Journal of Econometrics 115 (August 2003), p. 293-346 

 

First Version: October 2000 This Version: December 2002 

Download (pdf)

 

http://www.mit.edu/~vchern/ch_qbe.pdf

 

Abstract

 

This paper studies computationally and theoretically attractive estimators referred here as to the Laplace type estimators (LTE). The LTE include mans 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 LTEs 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.