Arbeitspapier
A Two-Step Estimator for Missing Values in Probit Model Covariates
This paper includes a simulation study on the bias and MSE properties of a two-step probit model estimator for handling missing values in covariates by conditional imputation. In one smaller simulation it is compared with an asymptotically efficient estimator and in one larger it is compared with the probit ML on complete cases after listwise deletion. Simulation results obtained favors the use of the two-step probit estimator and motivates further developments of the methodology.
- Language
-
Englisch
- Bibliographic citation
-
Series: Working Paper ; No. 3/2015
- Classification
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Wirtschaft
Econometrics
Multiple or Simultaneous Equation Models: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- Subject
-
binary variable
imputation
OLS
heteroskedasticity
- Event
-
Geistige Schöpfung
- (who)
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Laitila, Thomas
Wang, Lisha
- Event
-
Veröffentlichung
- (who)
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Örebro University School of Business
- (where)
-
Örebro
- (when)
-
2015
- Handle
- Last update
-
10.03.2025, 11:41 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the 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
- Laitila, Thomas
- Wang, Lisha
- Örebro University School of Business
Time of origin
- 2015
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