Arbeitspapier
High-dimensional econometrics and regularized GMM
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.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP35/18
- Classification
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Wirtschaft
- Event
-
Geistige Schöpfung
- (who)
-
Belloni, Alexandre
Chernozhukov, Victor
Chetverikov, Denis
Hansen, Christian Bailey
Kato, Kengo
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
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2018
- DOI
-
doi:10.1920/wp.cem.2018.3518
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Belloni, Alexandre
- Chernozhukov, Victor
- Chetverikov, Denis
- Hansen, Christian Bailey
- Kato, Kengo
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2018
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Plug-in regularized estimation of high-dimensional parameters in nonlinear semiparametric models
L1-Penalised quantile regression in high-dimensional sparse models
Post-l1-penalized estimators in high-dimensional linear regression models
hdm: High-dimensional metrics
Double/de-biased machine learning using regularized Riesz representers
Inference for high-dimensional sparse econometric models
Estimation of treatment effects with high-dimensional controls
Robust inference in high-dimensional approximately sparse quantile regression models
Inference on treatment effects after selection amongst high-dimensional controls
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
Program evaluation with high-dimensional data
Program evaluation with high-dimensional data
Plug-in regularized estimation of high-dimensional parameters in nonlinear semiparametric models
L1-Penalised quantile regression in high-dimensional sparse models
Post-l1-penalized estimators in high-dimensional linear regression models
hdm: High-dimensional metrics
Double/de-biased machine learning using regularized Riesz representers
Inference for high-dimensional sparse econometric models
Estimation of treatment effects with high-dimensional controls
Robust inference in high-dimensional approximately sparse quantile regression models
Inference on treatment effects after selection amongst high-dimensional controls
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
Program evaluation with high-dimensional data