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Testing-Based Forward Model Selection

Kozbur, Damian (2017). Testing-Based Forward Model Selection. American Economic Review, 107(5):266-269.

Abstract

This paper defines and studies a variable selection procedure called Testing-Based Forward Model Selection. The procedure inductively selects covariates which increase predictive accuracy into a working statistical regression model until a stopping criterion is met. The stopping criteria and selection criteria are defined using statistical hypothesis tests. The paper explicitly describes a testing procedure in the context of high-dimensional linear regression with heteroskedastic disturbances. Finally, a simulation study examines finite sample performance of the proposed procedure and shows that it behaves favorably in high-dimensional sparse settings in terms of prediction error and size of selected model.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Social Sciences & Humanities > Economics and Econometrics
Uncontrolled Keywords:Model evaluation, validation, and selection
Scope:Discipline-based scholarship (basic research)
Language:English
Date:May 2017
Deposited On:24 May 2017 13:10
Last Modified:17 Jan 2025 02:35
Publisher:American Economic Association
ISSN:0002-8282
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1257/aer.p20171039
Other Identification Number:merlin-id:14849
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