L1-Penalized Quantile Regression in High Dimensional Sparse Models
By: [by] Victor Chernozhukov [and] Alexandre Belloni
Contributors: Belloni, Alexandre; Massachusetts Institute of Technology. Dept. of Economics
Series: Working paper series / Massachusetts Institute of Technology, Dept. of Economics -- working paper 09-11; Working paper (Massachusetts Institute of Technology. Dept. of Economics) -- no. 09-11.
Publisher: Massachusetts Institute of Technology, Dept. of Economics
Published: 2009
Place of publication: Cambridge, MA
Language: eng
Number of pages: 60
Description: We consider median regression and, more generally, quantile regression in high-dimensional sparse models. In these models the overall number of regressors p is very large, possibly larger than the sample size n, but only s of these regressors have non-zero impact on the conditional quantile of the response variable, where s grows slower than n. Since in this case the ordinary quantile regression is not consistent, we consider quantile regression penalized by the 1-norm of coefficients (L1-QR). First, we show that L1-QR is consistent, up to a logarithmic factor, at the oracle rate which is achievable when the minimal true model is known. The overall number of regressors p affects the rate only through a logarithmic factor, thus allowing nearly exponential growth in the number of zero-impact regressors. The rate result holds under relatively weak conditions, requiring that s/n converges to zero at a super-logarithmic speed and that regularization parameter satisfies certain theoretical constraints. Second, we propose a pivotal, data-driven choice of the regularization parameter and show that it satisfies these theoretical constraints. Third, we show that L1-QR correctly selects the true minimal model as a valid submodel, when the non-zero coefficients of the true model are well separated from zero. We also show that the number of non-zero coefficients in L1-QR is of same stochastic order as s, the number of non-zero coefficients in the minimal true model. Fourth, we analyze the rate of convergence of a two-step estimator that applies ordinary quantile regression to the selected model. Fifth, we evaluate the performance of L1-QR in a Monte-Carlo experiment, and provide an application to the analysis of the international economic growth. Keywords: median regression, quantile regression, sparse models. JEL Classifications: C13, C14, C30, C51, D4, J24, J31.
Notes: "April 24, 2009."

Includes bibliographical references (p. 58-60).

Abstract in HTML and working paper for download in PDF available via World Wide Web at the Social Science Research Network.