Arbeitspapier
Estimating Selection Models without Instrument with Stata
This article presents the eqregsel command for implementing the estimation and bootstrap inference of sample selection models via extremal quantile regression. The command estimates a semiparametric sample selection model without instrument or large support regressor, and outputs the point estimates of the homogenous linear coefficients, their bootstrap standard errors, as well as the p-value for a specification test.
- Sprache
-
Englisch
- Erschienen in
-
Series: IZA Discussion Papers ; No. 12486
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
Econometric Software
Wage Level and Structure; Wage Differentials
- Thema
-
extremal quantile regressions
sample selection models
eqregsel
- Ereignis
-
Geistige Schöpfung
- (wer)
-
D'Haultfœuille, Xavier
Maurel, Arnaud
Qiu, Xiaoyun
Zhang, Yichong
- Ereignis
-
Veröffentlichung
- (wer)
-
Institute of Labor Economics (IZA)
- (wo)
-
Bonn
- (wann)
-
2019
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
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
- D'Haultfœuille, Xavier
- Maurel, Arnaud
- Qiu, Xiaoyun
- Zhang, Yichong
- Institute of Labor Economics (IZA)
Entstanden
- 2019
Ähnliche Objekte (12)
Selection without exclusion
Iassopack: Model Selection and Prediction with Regularized Regression in Stata
Einführung in STATA
Estimating Fourier-sparse signals without a frequency gap
Lee’s treatment effect bounds for non-random sample selection - an implementation in Stata
Estimating the gravity model without gravity using panel data
Estimating the gravity model without gravity using panel data
Estimating a semi-parametric duration model without specifying heterogeneity
Estimating a semi-parametric duration model without specifying heterogeneity
Stata tip 129: efficiently processing textual data with Stata’s new Unicode features
Stata tip 129: efficiently processing textual data with Stata’s new Unicode features
Implementing intersection bounds in Stata
Selection without exclusion
Iassopack: Model Selection and Prediction with Regularized Regression in Stata
Einführung in STATA
Estimating Fourier-sparse signals without a frequency gap
Lee’s treatment effect bounds for non-random sample selection - an implementation in Stata
Estimating the gravity model without gravity using panel data
Estimating the gravity model without gravity using panel data
Estimating a semi-parametric duration model without specifying heterogeneity
Estimating a semi-parametric duration model without specifying heterogeneity
Stata tip 129: efficiently processing textual data with Stata’s new Unicode features
Stata tip 129: efficiently processing textual data with Stata’s new Unicode features
Implementing intersection bounds in Stata
Selection without exclusion
Iassopack: Model Selection and Prediction with Regularized Regression in Stata
Einführung in STATA
Estimating Fourier-sparse signals without a frequency gap
Lee’s treatment effect bounds for non-random sample selection - an implementation in Stata
Estimating the gravity model without gravity using panel data
Estimating the gravity model without gravity using panel data
Estimating a semi-parametric duration model without specifying heterogeneity
Estimating a semi-parametric duration model without specifying heterogeneity
Stata tip 129: efficiently processing textual data with Stata’s new Unicode features
Stata tip 129: efficiently processing textual data with Stata’s new Unicode features