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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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Impact of Diagnostic Tests

Impact of Diagnostic Tests

Chapter:
(p.151) 12 Impact of Diagnostic Tests
Source:
Empirical Model Discovery and Theory Evaluation
Author(s):

David F. Hendry

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262028356.003.0012

Chapter 7 considered the main mis-specification tests in Gets model selection using an information taxonomy of past, present and future data, theory and measurement information and rival models. The first seeks a homoskedastic innovation error {ϵt}; the second weak exogeneity of conditioning variables for the parameters of interest ϴ (say); the third, constant, invariant parameters, ϴ; the fourth theory consistent, identifiable structures; the fifth data-admissible formulations on accurate observations; and the sixth, encompassing rival models. We now address the specific mis-specification tests used in Autometrics to determine congruence, and consider their operating characteristics when applied to the DGP, the GUM and the finally selected model. We also examine the impact of their repeated use as diagnostic checks to ensure that reductions maintain congruence.

Keywords:   Diagnostic tests, information taxonomy, repeated testing, selection effects

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