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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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. Comparisons of Autometrics with Other Approaches

. Comparisons of Autometrics with Other Approaches

Chapter:
(p.203) 17. Comparisons of Autometrics with Other Approaches
Source:
Empirical Model Discovery and Theory Evaluation
Author(s):

David F. Hendry

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262028356.003.0017

There are many possible methods of model selection, most of which can be implemented in an automatic algorithm. General contenders include single path approaches such as forward and backward selection, mixed variants like step-wise, multi-path search methods including Hoover and Perez (1999) and PcGets, information criteria (AIC, BIC etc.), Lasso, and RETINA, as well as a number of selection algorithms specifically designed for a forecasting context (such as PIC: see e.g., Phillips, 1995, 1996, and Phillips and Ploberger, 1996). Here we only consider the former group of methods in relation to Autometrics, partly to evaluate improvements over time in the conventional selection aspects of its performance. Three key findings are that Autometrics does indeed deliver substantive improvements in many settings; that performance is not necessarily adversely affected by having more variables than observations; and that although other approaches sometimes outperform, they are not reliable and can also deliver very poor results, whereas Autometrics tends to perform similarly to commencing from the LDGP using the same significance level.

Keywords:   Autometrics, step-wise regression, information criteria, Lasso, RETINA

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