Jump to ContentJump to Main Navigation
Empirical Model Discovery and Theory EvaluationAutomatic Selection Methods in Econometrics$
Users without a subscription are not able to see the full content.

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

Show Summary Details
Page of

PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 03 July 2022

Selecting Non-Linear Models

Selecting Non-Linear Models

Chapter:
(p.253) 21 Selecting Non-linear Models
Source:
Empirical Model Discovery and Theory Evaluation
Author(s):

David F. Hendry

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262028356.003.0021

The selection of a non-linear model often begins from a previous linear model and adds non-linear terms. Such an approach is specific-to-general in two respects. First, between studies, advances are bound to be generalizations as new knowledge accumulates, which is in part why scientific progress is so difficult. Second, however, one should not just extend the best earlier model, which was implicitly selected to accommodate all omitted effects, but commence with an identified and congruent general non-linear approximation which enters all the linear terms unrestrictedly and includes a complete set of impulse indicators so that non-linearities do not mis-represent breaks or outliers. As a prior step, a test for non-linearity can check whether any extension is needed. We then use squares, cubics, and exponentials of the principal components of the variables to approximate a range of possible non-linearities. Once a selection has been made therefrom, if non-linear terms remain, any proposed theory-based functions (such as logistic or squashing) can be entered to check if they further simplify the approximation. Such an approach avoids the issue of lack of identification under the null and directly tests that the postulated functions are valid reductions.

Keywords:   Non-linearities, principal components, approximating functions, encompassing

MIT Press Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs, and if you can't find the answer there, please contact us.