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Empirical Model Discovery and Theory EvaluationAutomatic Selection Methods in Econometrics$
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David F. Hendry and Jurgen A. Doornik

Print publication date: 2014

Print ISBN-13: 9780262028356

Published to MIT Press Scholarship Online: January 2015

DOI: 10.7551/mitpress/9780262028356.001.0001

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Evaluating Model Selection

Evaluating Model Selection

Chapter:
(p.71) 5 Evaluating Model Selection
Source:
Empirical Model Discovery and Theory Evaluation
Author(s):

David F. Hendry

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262028356.003.0005

Empirical models could be chosen according to many criteria, as this chapter discusses. Selection criteria can conflict, such as achieving empirical congruence may thwart theory consistency and vice versa, so we consider nine possible ways to judge the success of selection. Of these, four seem infeasible, two are widely used but seem suspect, so we focus on the remaining three practical criteria, namely a selection algorithm’s ability to recover the local data-generating process (LDGP) starting from the general unrestricted model as often as when starting from the LDGP itself; whether the operating characteristics of the algorithm match their desired properties; and whether selection can almost always find a well-specified, undominated model of the LDGP. Achieving all three jointly seems feasible.

Keywords:   Model selection criteria, evaluating selection, search algorithm properties

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