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Replication data for: Testing-Based Forward Model Selection
- Kozbur, Damian
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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.
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Is supplement to
DOI: 10.1257/aer.p20171039 (Text)
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Kozbur, Damian. American Economic Review, American Economic Review, 107, no. 5 (n.d.): 266–69. https://doi.org/10.1257/aer.p20171039.
- ID: 10.1257/aer.p20171039 (DOI)
Update Metadata: 2020-05-18 | Issue Number: 2 | Registration Date: 2019-10-12
Kozbur, Damian (2017): Replication data for: Testing-Based Forward Model Selection. Version: V0. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E113506
