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Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments

Authors: Victor Chernozhukov , Christian Hansen and Martin Spindler
Date: 01 May 2015
Type: Journal Article, American Economic Review, Vol. 105, No. 5, pp. 486-90
DOI: 10.1257/aer.p20151022

Abstract

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.

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Victor Chernozhukov, Christian Hansen and Martin Spindler January 2015, Post-selection and post-regularization inference in linear models with many controls and instruments, cemmap Working Paper, CWP02/15, IFS

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