Replication data for: Testing-Based Forward Model Selection

Principal Investigator(s):  View help for Principal Investigator(s) Damian Kozbur

Version:  View help for Version V1

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:  View help for 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:  View help for JEL Classification
      C52 Model Evaluation, Validation, and Selection


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V1 [2019-10-12]

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