Arbeitspapier
Nonparametric identification of regression models containing a misclassified dichotomous regressor without instruments
This note considers nonparametric identification of a general nonlinear regression model with a dichotomous regressor subject to misclassification error. The available sample information consists of a dependent variable and a set of regressors, one of which is binary and error-ridden with misclassification error that has unknown distribution. Our identification strategy does not parameterize any regression or distribution functions, and does not require additional sample information such as instrumental variables, repeated measurements, or an auxiliary sample. Our main identifying assumption is that the regression model error has zero conditional third moment. The results include a closed-form solution for the unknown distributions and the regression function.
- Language
-
Englisch
- Bibliographic citation
-
Series: cemmap working paper ; No. CWP17/07
- Classification
-
Wirtschaft
- Subject
-
misclassification error , identification , nonparametric regression
Regression
Statistischer Fehler
Nichtparametrisches Verfahren
- Event
-
Geistige Schöpfung
- (who)
-
Chen, Xiaohong
Hu, Yingyao
Lewbel, Arthur
- Event
-
Veröffentlichung
- (who)
-
Centre for Microdata Methods and Practice (cemmap)
- (where)
-
London
- (when)
-
2007
- DOI
-
doi:10.1920/wp.cem.2007.1707
- Handle
- Last update
-
20.09.2024, 8:23 AM CEST
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
- Chen, Xiaohong
- Hu, Yingyao
- Lewbel, Arthur
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2007
Other Objects (12)
Nonparametric nonstationary regression
Nonparametric identification of the classical errors-in-variables model without side information
Efficient Bayesian nonparametric hazard regression
Confidence regions, non-parametric regression
R robustified additive nonparametric regression
Efficient Bayesian Nonparametric Hazard Regression
M Robustified Additive Nonparametric Regression
Nonparametric regression with common shocks
M robustified additive nonparametric regression
R robustified additive nonparametric regression
Nonparametric identification and semiparametric estimation of classical measurement error models without side information
Nonparametric identification and estimation of nonclassical errors-in-variablles models without additional information
Nonparametric nonstationary regression
Nonparametric identification of the classical errors-in-variables model without side information
Efficient Bayesian nonparametric hazard regression
Confidence regions, non-parametric regression
R robustified additive nonparametric regression
Efficient Bayesian Nonparametric Hazard Regression
M Robustified Additive Nonparametric Regression
Nonparametric regression with common shocks
M robustified additive nonparametric regression
R robustified additive nonparametric regression
Nonparametric identification and semiparametric estimation of classical measurement error models without side information
Nonparametric identification and estimation of nonclassical errors-in-variablles models without additional information
Nonparametric nonstationary regression
Nonparametric identification of the classical errors-in-variables model without side information
Efficient Bayesian nonparametric hazard regression
Confidence regions, non-parametric regression
R robustified additive nonparametric regression
Efficient Bayesian Nonparametric Hazard Regression
M Robustified Additive Nonparametric Regression
Nonparametric regression with common shocks
M robustified additive nonparametric regression
R robustified additive nonparametric regression
Nonparametric identification and semiparametric estimation of classical measurement error models without side information