Variable selection and estimation in high-dimensional models

Joel L. Horowitz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Models with high-dimensional covariates arise frequently in economics and other fields. Often, only a few covariates have important effects on the dependent variable. When this happens, the model is said to be sparse. In applications, however, it is not known which covariates are important and which are not. This paper reviews methods for discriminating between important and unimportant covariates with particular attention given to methods that discriminate correctly with probability approaching 1 as the sample size increases. Methods are available for a wide variety of linear, nonlinear, semiparametric and nonparametric models. The performance of some of these methods in finite samples is illustrated through Monte Carlo simulations and an empirical example.

Original languageEnglish (US)
Pages (from-to)389-407
Number of pages19
JournalCanadian Journal of Economics
Volume48
Issue number2
DOIs
StatePublished - May 1 2015

ASJC Scopus subject areas

  • Economics and Econometrics

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