High-dimensional econometrics and regularized GMM
Denis Chetverikov (),
Christian Hansen and
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Alexandre Belloni: Institute for Fiscal Studies
Denis Chetverikov: Institute for Fiscal Studies and UCLA
Christian Hansen: Institute for Fiscal Studies and Chicago GSB
Kengo Kato: Institute for Fiscal Studies
No CWP35/18, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies
This chapter presents key concepts and theoretical results for analyzing estimation and inference in high-dimensional models. High-dimensional models are characterized by having a number of unknown parameters that is not vanishingly small relative to the sample size. We first present results in a framework where estimators of parameters of interest may be represented directly as approximate means. Within this context, we review fundamental results including high-dimensional central limit theorems, bootstrap approximation of high-dimensional limit distributions, and moderate deviation theory. We also review key concepts underlying inference when many parameters are of interest such as multiple testing with family-wise error rate or false discovery rate control. We then turn to a general high-dimensional minimum distance framework with a special focus on generalized method of moments problems where we present results for estimation and inference about model parameters. The presented results cover a wide array of econometric applications, and we discuss several leading special cases including high-dimensional linear regression and linear instrumental variables models to illustrate the general results.
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Working Paper: High-Dimensional Econometrics and Regularized GMM (2018)
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