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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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More Variables than Observations

More Variables than Observations

Chapter:
(p.233) 19 More Variables than Observations
Source:
Empirical Model Discovery and Theory Evaluation
Author(s):

David F. Hendry

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262028356.003.0019

We now move to also using Autometrics as a new way of thinking about a range of problems previously deemed almost intractable. In this part, we consider five major areas. First, the approach of impulse-indicator saturation leads in this chapter to ways of handling excess numbers of variables, N > T, based on a mixture of reduction and expansion steps, with an empirical illustration. Secondly, IIS also allows the investigation of multiple breaks, addressed in chapter 20. Third, chapter 21 considers selecting nonlinear models, which raise some new issues, and also often involve more candidate variables than observations. In turn, applying IIS to detect breaks in models of marginal processes, then testing the relevance of the retained indicators in conditional equations leads to a new test for super exogeneity, described in chapter 22. Finally, chapter 23 discusses selecting forecasting models. Throughout, we use automatic model selection as a new instrument, which changes how to think about existing problems, and suggests novel solutions.

Keywords:   More variables than observations, reduction and expansion steps, Hoover–Perez

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