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



(p.3) 1 Introduction
Empirical Model Discovery and Theory Evaluation

David F. Hendry

The MIT Press

This chapter provides an overview of the book. Models of empirical phenomena are needed for four main reasons: understanding the evolution of data processes, testing subject-matter theories, forecasting future outcomes, and conducting policy analyses. All four intrinsically involve discovery, since many features of all economic models lie outside the purview of prior reasoning, theoretical analyses or existing evidence. Economies are so high dimensional, evolutionary from many sources of innovation, and non-constant from intermittent, often unanticipated, shifts that discovering their properties is the key objective of empirical modeling. Automatic selection methods can outperform experts in formulating models when there are many candidate variables, possibly long lag lengths, potential non-linearities, and outliers, data contamination, or parameter shifts of unknown magnitudes at unknown time points. They also outperform manual selection by their ability to explore many search paths and so handle many variables—even more than the number of observations—yet have high success rates. Despite selecting from large numbers of candidate variables, automatic selection methods can achieve desired targets for incorrectly retaining irrelevant variables, and still deliver near unbiased estimates of policy relevant parameters. Finally, they can automatically conduct a range of pertinent tests of specification and mis-specification. To do so, a carefully structured search is required from a general model that contains all the substantively relevant features, an approach known as general-to-specific, with the abbreviation Gets. This chapter introduces some of the key concepts, developed in more detail later.

Keywords:   Testing theories, Forecasting, policy analyses, automatic model selection, general-to-specific

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.