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Valid post-selection and post-regularization inference: An elementary, general approach

Authors: Victor Chernozhukov , Christian Hansen and Martin Spindler
Date: 01 August 2015
Type: Journal Article, Annual Review of Economics, Vol. 7, No. 1, pp. 649--688
DOI: 10.1146/annurev-economics-012315-015826

Abstract

We present an expository, general analysis of valid post-selection or post-regularization inference about a low-dimensional target parameter in the presence of a very high-dimensional nuisance parameter that is estimated using selection or regularization methods. Our analysis provides a set of high-level conditions under which inference for the low-dimensional parameter based on testing or point estimation methods will be regular despite selection or regularization biases occurring in the estimation of the high-dimensional nuisance parameter. A key element is the use of so-called immunized or orthogonal estimating equations that are locally insensitive to small mistakes in the estimation of the high-dimensional nuisance parameter. As an illustration, we analyze affine-quadratic models and specialize these results to a linear instrumental variables model with many regressors and many instruments. We conclude with a review of other developments in post-selection inference and note that many can be viewed as special cases of the general encompassing framework of orthogonal estimating equations provided in this article.

Previous version:
Victor Chernozhukov, Christian Hansen and Martin Spindler August 2016, Valid post-selection and post-regularization inference: An elementary, general approach, cemmap Working Paper, CWP36/16, The IFS

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