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An MCMC Approach to Classical EstimationAuthorsVictor 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. |
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