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Variable selection and estimation in high-dimensional models

Authors: Joel L. Horowitz
Date: 01 May 2015
Type: Journal Article, Canadian Journal of Economics/Revue Canadienne d'économique, Vol. 48, No. 2, pp. 389--407
DOI: 10.1111/caje.12130

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.

Previous version:
Joel L. Horowitz July 2015, Variable selection and estimation in high-dimensional models, cemmap Working Paper, CWP35/15, Institute for Fiscal Studies

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