Replication data for: Testing-Based Forward Model Selection

Name	File Type	Size	Last Modified
LICENSE.txt	text/plain	14.6 KB	10/12/2019 07:29:AM
Simulation_FSel_PandP_2017_Kozbur.m	text/plain	5.8 KB	10/12/2019 07:29:AM
Simulation_Readme_Kozbur.pdf	application/pdf	30.9 KB	10/12/2019 07:29:AM

Project Citation:

Kozbur, Damian. Replication data for: Testing-Based Forward Model Selection. Nashville, TN: American Economic Association [publisher], 2017. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-12. https://doi.org/10.3886/E113506V1

Project Description

Summary: 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.

Scope of Project

JEL Classification:
C52 Model Evaluation, Validation, and Selection

Related Publications

Download this project

Published Versions

V1 [2019-10-12]

Export Metadata

Dublin Core

DDI 2.5

Report a Problem

Found a serious problem with the data, such as disclosure risk or copyrighted content? Let us know.

This material is distributed exactly as it arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigator(s) if further information is desired.