Bayesian Method of Moments (BMOM) Analysis of Parametric and Semiparametric Regression Models (1997)
| Venue: | South African Statistical Journal |
| Citations: | 2 - 0 self |
BibTeX
@ARTICLE{Zellner97bayesianmethod,
author = {Arnold Zellner and Justin Tobias and Hang K. Ryu},
title = {Bayesian Method of Moments (BMOM) Analysis of Parametric and Semiparametric Regression Models},
journal = {South African Statistical Journal},
year = {1997},
volume = {31},
pages = {41--69}
}
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Abstract
The Bayesian Method of Moments is applied to semiparametric regression models using alternative series expansions of an unknown regression function. We describe estimation loss functions, predictive loss functions and posterior odds as techniques to determine how many terms in a particular expansion to keep and how to choose among different types of expansions. The developed theory is then applied in a Monte-Carlo experiment to data generated from a CES production function. 1 Introduction In this paper, we take up the Bayesian Method of Moments (BMOM) analysis of parametric and semiparametric models. In previous work, Zellner (1994, 1995, 1996, 1997), Zellner and Sacks (1996), Tobias and Zellner (1997), Green and Strawderman (1996) and Currie (1996), the BMOM approach has been described and applied to parametric models. University of Chicago, University of Chicago, and Chung Ang University, respectively. Research financed in part by the National Science Foundation and by income ...
