Header

UZH-Logo

Maintenance Infos

Analysis of testing‐based forward model selection


Kozbur, Damian (2020). Analysis of testing‐based forward model selection. Econometrica, 88(5):2147-2173.

Abstract

This paper analyzes a procedure called Testing‐Based Forward Model Selection (TBFMS) in linear regression problems. This procedure inductively selects covariates that add predictive power into a working statistical model before estimating a final regression. The criterion for deciding which covariate to include next and when to stop including covariates is derived from a profile of traditional statistical hypothesis tests. This paper proves probabilistic bounds, which depend on the quality of the tests, for prediction error and the number of selected covariates. As an example, the bounds are then specialized to a case with heteroscedastic data, with tests constructed with the help of Huber–Eicker–White standard errors. Under the assumed regularity conditions, these tests lead to estimation convergence rates matching other common high‐dimensional estimators including Lasso.

Abstract

This paper analyzes a procedure called Testing‐Based Forward Model Selection (TBFMS) in linear regression problems. This procedure inductively selects covariates that add predictive power into a working statistical model before estimating a final regression. The criterion for deciding which covariate to include next and when to stop including covariates is derived from a profile of traditional statistical hypothesis tests. This paper proves probabilistic bounds, which depend on the quality of the tests, for prediction error and the number of selected covariates. As an example, the bounds are then specialized to a case with heteroscedastic data, with tests constructed with the help of Huber–Eicker–White standard errors. Under the assumed regularity conditions, these tests lead to estimation convergence rates matching other common high‐dimensional estimators including Lasso.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Downloads

5 downloads since deposited on 07 Oct 2020
5 downloads since 12 months
Detailed statistics

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 selection, forward regression, sparsity, hypothesis testing
Language:English
Date:September 2020
Deposited On:07 Oct 2020 15:43
Last Modified:08 Oct 2020 20:00
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0012-9682
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.3982/ecta16273

Download

Hybrid Open Access

Download PDF  'Analysis of testing‐based forward model selection'.
Preview
Content: Published Version
Language: English
Filetype: PDF
Size: 239kB
View at publisher