An MCMC approach to classical estimation
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Author
Contributions
- Hong, Han - Contributor
- Massachusetts Institute of Technology. Dept. of Economics - Contributor
Publication
2002 - Massachusetts Institute of Technology, Dept. of Economics, Cambridge, MA, Massachusetts
Language
English
Word Count
12,250 words, Guess
Page Count
49 pages
Identifiers
- Internet Archivemcmcapproachtocl00cher
- OCLC Control Number53190628
- Open LibraryOL24640019M
Alternate 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.
Subjects
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