American Economic Review
ISSN 0002-8282 (Print) | ISSN 1944-7981 (Online)
Testing-Based Forward Model Selection
American Economic Review
vol. 107,
no. 5, May 2017
(pp. 266–69)
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.Citation
Kozbur, Damian. 2017. "Testing-Based Forward Model Selection." American Economic Review, 107 (5): 266–69. DOI: 10.1257/aer.p20171039Additional Materials
JEL Classification
- C52 Model Evaluation, Validation, and Selection