Journal article
THE FACTOR-LASSO AND K-STEP BOOTSTRAP APPROACH FOR INFERENCE IN HIGH-DIMENSIONAL ECONOMIC APPLICATIONS
Econometric theory, Vol.35(3), pp.465-509
06/01/2019
DOI: 10.1017/S0266466618000245
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 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 inferential procedure based on factor extraction followed by lasso regression and show that the procedure has good asymptotic properties. We also provide a simple k-step bootstrap procedure that may be used to construct inferential statements about the low-dimensional parameters of interest and prove its asymptotic validity. We provide simulation evidence about the performance of our procedure and illustrate its use in an empirical application.
Details
- Title: Subtitle
- THE FACTOR-LASSO AND K-STEP BOOTSTRAP APPROACH FOR INFERENCE IN HIGH-DIMENSIONAL ECONOMIC APPLICATIONS
- Creators
- Christian Hansen - University of ChicagoYuan Liao - Rutgers, The State University of New Jersey
- Resource Type
- Journal article
- Publication Details
- Econometric theory, Vol.35(3), pp.465-509
- DOI
- 10.1017/S0266466618000245
- ISSN
- 0266-4666
- eISSN
- 1469-4360
- Publisher
- Cambridge Univ Press
- Number of pages
- 45
- Grant note
- University of Chicago Booth School of Business; University of Chicago 1558636 / Direct For Social, Behav & Economic Scie; Divn Of Social and Economic Sciences; National Science Foundation (NSF); NSF - Directorate for Social, Behavioral & Economic Sciences (SBE) 1558636 / National Science Foundation; National Science Foundation (NSF)
- Language
- English
- Date published
- 06/01/2019
- Academic Unit
- Economics
- Record Identifier
- 9984936841402771
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