 Some Recent Developments in Econometric InferenceEconometric Reviews, 2003, vol. 22, issue 2, 203-215 Abstract: Recent results in information theory, see Soofi (1996; 2001) for a review, include derivations of optimal information processing rules, including Bayes' theorem, for learning from data based on minimizing a criterion functional, namely output information minus input information as shown in Zellner (1988; 1991; 1997; 2002). Herein, solution post data densities for parameters are obtained and studied for cases in which the input information is that in (1) a likelihood function and a prior density; (2) only a likelihood function; and (3) neither a prior nor a likelihood function but only input information in the form of post data moments of parameters, as in the Bayesian method of moments approach. Then it is shown how optimal output densities can be employed to obtain predictive densities and optimal, finite sample structural coefficient estimates using three alternative loss functions. Such optimal estimates are compared with usual estimates, e.g., maximum likelihood, two-stage least squares, ordinary least squares, etc. Some Monte Carlo experimental results in the literature are discussed and implications for the future are provided. Keywords: Econometric inference; Bayes' theorem; Information theory; Learning; Optimal estimation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:emetrv:v:22:y:2003:i:2:p:203-215 Ordering information: This journal article can be ordered from Access Statistics for this article Econometric Reviews is currently edited by Dr. Essie Maasoumi More articles in Econometric Reviews from Taylor & Francis Journals |
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