Citations (39)



An MCMC Approach to Classical Estimation

Victor Chernozhukov

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

Han Hong


December 2002

MIT Department of Economics Working Paper No. 03-21

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.

Number of Pages in PDF File: 55

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

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Date posted: July 15, 2003  

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

Contact Information

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
Han Hong
No Address Available
Feedback to SSRN

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