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

Victor Chernozhukov, Christian Hansen and Martin Spindler

Papers from arXiv.org

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.

Date: 2015-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (85)

Downloads: (external link)
http://arxiv.org/pdf/1501.03185 Latest version (application/pdf)

Related works:
Journal Article: Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments (2015)
Working Paper: Post-selection and post-regularization inference in linear models with many controls and instruments (2015)
Working Paper: Post-selection and post-regularization inference in linear models with many controls and instruments (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1501.03185

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().