An MCMC approach to classical estimation
By: Victor Chernozhukov [and] Han Hong
Contributors: Hong, Han; Massachusetts Institute of Technology. Dept. of Economics
Series: Working paper series / Massachusetts Institute of Technology, Dept. of Economics -- working paper 03-21; Working paper (Massachusetts Institute of Technology. Dept. of Economics) -- no. 03-21.
Publisher: Massachusetts Institute of Technology, Dept. of Economics
Published: 2002
Place of publication: Cambridge, MA
Language: eng
Number of pages: 49
Other titles: Markov chain Monte Carlo approach to classical estimation
Description: 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.
Notes: "December 2002." -- First version: October 2000; this version: December 2002"--P. [1]

Includes bibliographical references (p. 45-49).

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