Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Time Series Econometrics

Editor-in-Chief: Hidalgo, Javier

CiteScore 2017: 0.25

SCImago Journal Rank (SJR) 2017: 0.236
Source Normalized Impact per Paper (SNIP) 2017: 0.682

See all formats and pricing
More options …

Evaluating Automatic Model Selection

Jennifer L. Castle / Jurgen A Doornik / David F. Hendry
Published Online: 2011-02-03 | DOI: https://doi.org/10.2202/1941-1928.1097

We outline a range of criteria for evaluating model selection approaches that have been used in the literature. Focusing on three key criteria, we evaluate automatically selecting the relevant variables in an econometric model from a large candidate set. General-to-specific selection is outlined for a regression model in orthogonal variables, where only one decision is required to select, irrespective of the number of regressors. Comparisons with an automated model selection algorithm, Autometrics (Doornik, 2009), show similar properties, but not restricted to orthogonal cases. Monte Carlo experiments examine the roles of post-selection bias corrections and diagnostic testing as well as evaluate selection in dynamic models by costs of search versus costs of inference.

Keywords: model selection; Autometrics; costs of search; costs of inference

About the article

Published Online: 2011-02-03

Citation Information: Journal of Time Series Econometrics, Volume 3, Issue 1, ISSN (Online) 1941-1928, DOI: https://doi.org/10.2202/1941-1928.1097.

Export Citation

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Loann Desboulets
Econometrics, 2018, Volume 6, Number 4, Page 45
Prasad Sankar Bhattacharya and Dimitrios D. Thomakos
Journal of Forecasting, 2018
Norman Gemmell, Derek Gill, and Loc Nguyen
New Zealand Economic Papers, 2018, Page 1
David F. Hendry
International Journal of Forecasting, 2017
Massimiliano Caporin and Francesco Poli
Econometrics, 2017, Volume 5, Number 3, Page 35
Felix Pretis, Lea Schneider, Jason E. Smerdon, and David F. Hendry
Journal of Economic Surveys, 2016, Volume 30, Number 3, Page 403
David F. Hendry, Grayham E. Mizon, and Steve Cook
Cogent Economics & Finance, 2016, Volume 4, Number 1, Page 1170096
Jennifer Castle, Jurgen Doornik, David Hendry, and Felix Pretis
Econometrics, 2015, Volume 3, Number 2, Page 240
David F. Hendry and Grayham E. Mizon
Global Policy, 2011, Volume 2, Number 2, Page 176
André K. Anundsen
Journal of Applied Econometrics, 2015, Volume 30, Number 1, Page 145
Jennifer L. Castle, Jurgen A. Doornik, David F. Hendry, and Ragnar Nymoen
Econometric Reviews, 2014, Volume 33, Number 5-6, Page 553
Jennifer L. Castle and David F. Hendry
Journal of Econometrics, 2014, Volume 178, Page 286
Ioannis Vlachos and Dimitris Kugiumtzis
Journal of Forecasting, 2013, Volume 32, Number 8, Page 685
Tom Kornstad, Ragnar Nymoen, and Terje Skjerpen
Economic Modelling, 2013, Volume 33, Page 572
Jennifer L. Castle, Michael P. Clements, and David F. Hendry
Journal of Econometrics, 2013, Volume 177, Number 2, Page 305
Ciaran Driver, Lorenzo Trapani, and Giovanni Urga
International Journal of Forecasting, 2013, Volume 29, Number 3, Page 367
Jennifer L. Castle, Jurgen A. Doornik, and David F. Hendry
Oxford Bulletin of Economics and Statistics, 2013, Volume 75, Number 1, Page 6
Jennifer L. Castle, Jurgen A. Doornik, and David F. Hendry
Journal of Econometrics, 2012, Volume 169, Number 2, Page 239
Jennifer L. Castle, Xiaochuan Qin, and W. Robert Reed
Journal of Economic Surveys, 2013, Volume 27, Number 2, Page 269

Comments (0)

Please log in or register to comment.
Log in