Further Results on Bayesian Method of Moments Analysis of the Multiple Regression Model
AbstractIn this article we extend previous BMOM results by showing how information about a variance parameter and its relation to regression coefficients produces a rich class of postdata densities for regression parameters. Prediction and model selection techniques are also described. We also discuss the well-documented link between cross-entropy and the average log odds and then use this criterion in an experiment to compare results obtained from BMOM and Bayes approaches using data generated from known models.
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Bibliographic InfoPaper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number 12021.
Date of creation: 01 Jan 2001
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Publication status: Published in International Economic Review 2001, vol. 42, pp. 121-139
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- Zellner, Arnold & Tobias, Justin, 2001. "Further Results on Bayesian Method of Moments Analysis of the Multiple Regression Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(1), pages 121-40, February.
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