An MCMC Approach to Classical Estimation

55 Pages Posted: 15 Jul 2003  

Victor Chernozhukov

Massachusetts Institute of Technology (MIT) - Department of Economics; New Economic School

Han Hong

Independent

Date Written: December 2002

Abstract

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

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

Victor Chernozhukov (Contact Author)

Massachusetts Institute of Technology (MIT) - Department of Economics ( email )

50 Memorial Drive
Room E52-262f
Cambridge, MA 02142
United States
617-253-4767 (Phone)
617-253-1330 (Fax)

HOME PAGE: http://www.mit.edu/~vchern/

New Economic School

47 Nakhimovsky Prospekt
Moscow, 117418
Russia

Han Hong

Independent

No Address Available

Paper statistics

Downloads
1,692
Rank
7,265
Abstract Views
8,861