The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications

Christian Hansen () and Yuan Liao ()
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Christian Hansen: Booth School of Business, University of Chicago
Yuan Liao: Department of Economics, Rutgers University

Departmental Working Papers from Rutgers University, Department of Economics

Abstract: We consider inference about coefficients on a small number of variables of interest in a linear panel data model with additive unobserved individual and time specific effects and a large number of additional time-varying confounding variables. We allow the number of these additional confounding variables to be larger than the sample size, and suppose that, in addition to unrestricted time and individual specific effects, these confounding variables are generated by a small number of common factors and high-dimensional weakly-dependent disturbances. We allow that both the factors and the disturbances are related to the outcome variable and other variables of interest. To make informative inference feasible, we impose that the contribution of the part of the confounding variables not captured by time specific effects, individual specific effects, or the common factors can be captured by a relatively small number of terms whose identities are unknown. Within this framework, we provide a convenient computational algorithm based on factor extraction followed by lasso regression for inference about parameters of interest and show that the resulting procedure has good asymptotic properties. We also provide a simple k-step bootstrap procedure that may be used to construct inferential statements about parameters of interest and prove its asymptotic validity. The proposed bootstrap may be of substantive independent interest outside of the present context as the proposed bootstrap may readily be adapted to other contexts involving inference after lasso variable selection and the proof of its validity requires some new technical arguments. We also provide simulation evidence about performance of our procedure and illustrate its use in two empirical applications.

Keywords: treatment effects; panel data (search for similar items in EconPapers)
JEL-codes: C33 (search for similar items in EconPapers)
Pages: 81 pages
Date: 2016-11-29
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (5)

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Related works:
Journal Article: THE FACTOR-LASSO AND K-STEP BOOTSTRAP APPROACH FOR INFERENCE IN HIGH-DIMENSIONAL ECONOMIC APPLICATIONS (2019)
Working Paper: The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications (2016)
Working Paper: The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:rut:rutres:201610

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