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arXiv · 1501.03185

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

Victor Chernozhukov·Christian Hansen·Martin Spindler
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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.

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Victor Chernozhukov, Christian Hansen, Martin Spindler. 2015-01-13. Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments. https://arxiv.org/abs/1501.03185

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