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A Bayesian Interpretation of Extremim Estimators

Mahmoud El-Gamal

Working papers from Wisconsin Madison - Social Systems

Abstract: Extremum estimation is typically an ad hoc semi-parametric estimation procedure which is only justified on the basis of the asymptotic properties of the estimators. For a fixed finite data set, consider a large number of investigations using different extremum estimators to estimate the same parameter vector. The resulting empirical distribution of point estimates can be shown to coincide with a Bayesian posterior measure on the parameter space induced by a minimum information procedure. The Bayesian interpretation serves a number of purposes ranging from lending legitimacy to the use of those procedures in small sample problems, to helping prove asymptotic properties by reference to Bayes central limit theorems, to laying a foundation for combining point estimates from various extremum estimation experiments for statistical decision processes.

Keywords: EVALUATION; ECONOMIC MODELS (search for similar items in EconPapers)
JEL-codes: C51 C52 (search for similar items in EconPapers)
Pages: 16 pages
Date: 1997
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