Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/64784 
Year of Publication: 
2011
Series/Report no.: 
cemmap working paper No. CWP41/11
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
This article is about estimation and inference methods for high dimensional sparse (HDS) regression models in econometrics. High dimensional sparse models arise in situations where many regressors (or series terms) are available and the regression function is well-approximated by a parsimonious, yet unknown set of regressors. The latter condition makes it possible to estimate the entire regression function effectively by searching for approximately the right set of regressors. We discuss methods for identifying this set of regressors and estimating their coefficients based on l1 -penalization and describe key theoretical results. In order to capture realistic practical situations, we expressly allow for imperfect selection of regressors and study the impact of this imperfect selection on estimation and inference results. We focus the main part of the article on the use of HDS models and methods in the instrumental variables model and the partially linear model. We present a set of novel inference results for these models and illustrate their use with applications to returns to schooling and growth regression.
Subjects: 
inference under imperfect model selection
structural effects
high-dimensional econometrics
instrumental regression
partially linear regression
returns-to-schooling
growth regression
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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