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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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Model Selection in Underspecified Settings

Model Selection in Underspecified Settings

(p.223) 18. Model Selection in Underspecified Settings
Empirical Model Discovery and Theory Evaluation

David F. Hendry

The MIT Press

Despite seeking to commence an empirical study from a general initial specification that nests the LDGP for the set of variables under analysis, the GUM may be an underspecification. Moreover, the selection of the variables to analyze could lead to a poor representation of the economic data generation process. In this setting, model selection, rather than just fitting a prior specification, may help. Impulse-indicator saturation can correct nonconstancies induced by location shifts in omitted variables that alter the intercepts of models. Since IIS is a robust estimation method, it can mitigate some of the adverse effects of induced location shifts when models are mis-specified. The chapter provides an analysis of a simple setting, a Monte Carlo study thereof, and an artificial data example to illustrate.

Keywords:   Underspecified models, non-constant coefficients, impulse-indicator saturation

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