Arbeitspapier
Quantile models with endogeneity
In this article, we review quantile models with endogeneity. We focus on models that achieve indentification through the use of instrumental variables and discuss conditions under which partial and point identification are obtained. We discuss key conditions, which include monotonicity and full-rank-type conditions, in detail. In providing this review, we update the identification results of Chernozhukov and Hansen (2005). We illustrate the modelling assumptions through economically motivated examples. We also briefly review the literature on estimation and inference.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP25/13
- Klassifikation
-
Wirtschaft
- Thema
-
identification
treatment effects
structural models
instrumental variables
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Chernozhukov, Victor
Hansen, Christian
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2013
- DOI
-
doi:10.1920/wp.cem.2013.2513
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:23 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Chernozhukov, Victor
- Hansen, Christian
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2013
Ähnliche Objekte (12)
L1-Penalised quantile regression in high-dimensional sparse models
Inference for extremal conditional quantile models, with an application to market and birthweight risks
Post-l1-penalized estimators in high-dimensional linear regression models
Quantile regression with censoring and endogeneity
Posterior inference in curved exponential families under increasing dimensions
On the computational complexity of MCMC-based estimators in large samples
Posterior inference in curved exponential families under increasing dimensions
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Robust inference in high-dimensional approximately sparse quantile regression models
Vector quantile regression
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
Quantile graphical models: Prediction and conditional independence with applications to systemic risk
L1-Penalised quantile regression in high-dimensional sparse models
Inference for extremal conditional quantile models, with an application to market and birthweight risks
Post-l1-penalized estimators in high-dimensional linear regression models
Quantile regression with censoring and endogeneity
Posterior inference in curved exponential families under increasing dimensions
On the computational complexity of MCMC-based estimators in large samples
Posterior inference in curved exponential families under increasing dimensions
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Robust inference in high-dimensional approximately sparse quantile regression models
Vector quantile regression
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
Quantile graphical models: Prediction and conditional independence with applications to systemic risk
L1-Penalised quantile regression in high-dimensional sparse models
Inference for extremal conditional quantile models, with an application to market and birthweight risks
Post-l1-penalized estimators in high-dimensional linear regression models
Quantile regression with censoring and endogeneity
Posterior inference in curved exponential families under increasing dimensions
On the computational complexity of MCMC-based estimators in large samples
Posterior inference in curved exponential families under increasing dimensions
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Robust inference in high-dimensional approximately sparse quantile regression models
Vector quantile regression
Valid post-selection inference in high-dimensional approximately sparse quantile regression models