Akaike-type criteria and the reliability of inference: Model selection versus statistical model specification
AbstractSince the 1990s, the Akaike Information Criterion (AIC) and its various modifications/extensions, including BIC, have found wide applicability in econometrics as objective procedures that can be used to select parsimonious statistical models. The aim of this paper is to argue that these model selection procedures invariably give rise to unreliable inferences, primarily because their choice within a prespecified family of models (a) assumes away the problem of model validation, and (b) ignores the relevant error probabilities. This paper argues for a return to the original statistical model specification problem, as envisaged by Fisher (1922), where the task is understood as one of selecting a statistical model in such a way as to render the particular data a truly typical realization of the stochastic process specified by the model in question. The key to addressing this problem is to replace trading goodness-of-fit against parsimony with statistical adequacy as the sole criterion for when a fitted model accounts for the regularities in the data.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 158 (2010)
Issue (Month): 2 (October)
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Web page: http://www.elsevier.com/locate/jeconom
Akaike Information Criterion AIC BIC GIC MDL Model selection Model specification Statistical adequacy Curve-fitting Mathematical approximation theory Simplicity Least-squares Gauss linear model Linear regression model AR(p) Mis-specification testing Respecification Double-use of data Infinite regress and circularity Pre-test bias Model averaging Reliability of inference;
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