Arbeitspapier
Inference for high-dimensional sparse econometric models
This article is about estimation and inference methods for high dimensional sparse (HDS) regression models in econometrics. High dimensional sparse models arise in situations where many regressors (or series terms) are available and the regression function is well-approximated by a parsimonious, yet unknown set of regressors. The latter condition makes it possible to estimate the entire regression function effectively by searching for approximately the right set of regressors. We discuss methods for identifying this set of regressors and estimating their coefficients based on l1 -penalization and describe key theoretical results. In order to capture realistic practical situations, we expressly allow for imperfect selection of regressors and study the impact of this imperfect selection on estimation and inference results. We focus the main part of the article on the use of HDS models and methods in the instrumental variables model and the partially linear model. We present a set of novel inference results for these models and illustrate their use with applications to returns to schooling and growth regression.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP41/11
- Klassifikation
-
Wirtschaft
- Thema
-
inference under imperfect model selection
structural effects
high-dimensional econometrics
instrumental regression
partially linear regression
returns-to-schooling
growth regression
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Belloni, Alexandre
Chernozhukov, Victor
Hansen, Christian
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2011
- DOI
-
doi:10.1920/wp.cem.2011.4111
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Belloni, Alexandre
- Chernozhukov, Victor
- Hansen, Christian
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2011
Ähnliche Objekte (12)
Robust inference in high-dimensional approximately sparse quantile regression models
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
L1-Penalised quantile regression in high-dimensional sparse models
Inference on treatment effects after selection amongst high-dimensional controls
Uniform inference in high-dimensional gaussian graphical models
Valid simultaneous inference in high-dimensional settings (with the HDM package for R)
Program evaluation and causal inference with high-dimensional data
Post-l1-penalized estimators in high-dimensional linear regression models
hdm: High-dimensional metrics
Posterior inference in curved exponential families under increasing dimensions
Posterior inference in curved exponential families under increasing dimensions
Estimation of treatment effects with high-dimensional controls
Robust inference in high-dimensional approximately sparse quantile regression models
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
L1-Penalised quantile regression in high-dimensional sparse models
Inference on treatment effects after selection amongst high-dimensional controls
Uniform inference in high-dimensional gaussian graphical models
Valid simultaneous inference in high-dimensional settings (with the HDM package for R)
Program evaluation and causal inference with high-dimensional data
Post-l1-penalized estimators in high-dimensional linear regression models
hdm: High-dimensional metrics
Posterior inference in curved exponential families under increasing dimensions
Posterior inference in curved exponential families under increasing dimensions
Estimation of treatment effects with high-dimensional controls
Robust inference in high-dimensional approximately sparse quantile regression models
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
L1-Penalised quantile regression in high-dimensional sparse models
Inference on treatment effects after selection amongst high-dimensional controls
Uniform inference in high-dimensional gaussian graphical models
Valid simultaneous inference in high-dimensional settings (with the HDM package for R)
Program evaluation and causal inference with high-dimensional data
Post-l1-penalized estimators in high-dimensional linear regression models
hdm: High-dimensional metrics
Posterior inference in curved exponential families under increasing dimensions
Posterior inference in curved exponential families under increasing dimensions