Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments
Author(s)
Hansen, Christian; Spindler, Martin; Chernozhukov, Victor V
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We consider estimation of and inference about coefficients on endogenous variables in a linear instrumental variables model where the number of instruments and exogenous control variables are each allowed to be larger than the sample size. We work within an approximately sparse framework that maintains that the signal available in the instruments and control variables may be effectively captured by a small number of the available variables. We provide a LASSO-based method for this setting which provides uniformly valid inference about the coefficients on endogenous variables. We illustrate the method through an application to demand estimation.
Date issued
2015-05Department
Massachusetts Institute of Technology. Department of EconomicsJournal
American Economic Review
Publisher
American Economic Association
Citation
Chernozhukov, Victor, Christian Hansen, and Martin Spindler. “ Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments .” American Economic Review 105, no. 5 (May 2015): 486–490. © 2017 American Economic Association.
Version: Final published version
ISSN
0002-8282