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An MCMC approach to classical estimation
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DOI：10.1016/S0304-4076(03)00100-3.png)
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This paper studies computationally and theoretically attractive estimators called here Laplace type estimators (LTEs), which include means and quantiles of quasi-posterior distributions defined as transformations of general (nonlikelihood-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 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. (C) 2003 Elsevier Science B.V. All rights reserved.
Keyword:
Laplace
Bayes
Markov chain Monte Carlo
GMM
instrumental regression
censored quantile regression
instrumental quantile regression
empirical likelihood
value-at-risk
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