Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments
Abstract
In this note, we offer an approach to estimating causal/structural parameters in the presence of many instruments and controls based on methods for estimating sparse high-dimensional models. We use these high-dimensional methods to select both which instruments and which control variables to use. The approach we take extends BCCH2012, which covers selection of instruments for IV models with a small number of controls, and extends BCH2014, which covers selection of controls in models where the variable of interest is exogenous conditional on observables, to accommodate both a large number of controls and a large number of instruments. We illustrate the approach with a simulation and an empirical example. Technical supporting material is available in a supplementary online appendix.
Keywords
Cite
@article{arxiv.1501.03185,
title = {Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments},
author = {Victor Chernozhukov and Christian Hansen and Martin Spindler},
journal= {arXiv preprint arXiv:1501.03185},
year = {2017}
}
Comments
American Economic Review 2015, Papers and Proceedings