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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Testing-Based Forward Model Selection
Organization Unit
  • Damian Kozbur
  • English
Institution University of Zurich
Series Name Working paper series / Department of Economics
Number 283
ISSN 1664-7041
Number of Pages 66
Date 2018
Abstract Text This paper introduces and 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 for prediction error and the number of selected covariates, which depend on the quality of the tests. The bounds are then specialized to a case with heteroskedastic data with tests derived from Huber-Eicker-White standard errors. TBFMS performance is compared to Lasso and Post-Lasso in simulation studies. TBFMS is then analyzed as a component into larger post-model selection estimation problems for structural economic parameters. Finally, TBFMS is used to illustrate an empirical application to estimating determinants of economic growth.
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Keywords Model selection, forward regression, sparsity, hypothesis testing, Modellwahl, Lineare Regression, Wahrscheinlichkeitsverteilung, Statistischer Test
Additional Information Revised version Auch erschienen in: arXiv: 1512.02666v6