Artikel
When outcome heterogeneously matters for selection: a generalized selection correction estimator
The classical Heckman (1976, 1979) selection correction estimator (heckit) is misspecified and inconsistent, if an interaction of the outcome variable with an explanatory variable matters for selection. To address this specification problem, a full information maximum likelihood (FIML) estimator and a simple two-step estimator are developed. Monte Carlo (MC) simulations illustrate that the bias of the ordinary heckit estimator is removed by these generalized estimation procedures. Along with OLS and ordinary heckit, we apply these estimators to data from a randomized trial that evaluates the effectiveness of financial incentives for reducing obesity. Estimation results indicate that the choice of the estimation procedure clearly matters.
- Language
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Englisch
- Bibliographic citation
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Journal: Applied Economics ; ISSN: 1466-4283 ; Volume: 46 ; Year: 2014 ; Issue: 7 ; Pages: 762-768 ; Milton Park, Abingdon: Taylor and Francis
- Classification
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Wirtschaft
Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
Field Experiments
- Subject
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selection bias
interaction
heterogeneity
generalized estimator
- Event
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Geistige Schöpfung
- (who)
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Reichert, Arndt
Tauchmann, Harald
- Event
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Veröffentlichung
- (who)
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Taylor and Francis
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften, Leibniz-Informationszentrum Wirtschaft
- (where)
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Milton Park, Abingdon
- (when)
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2014
- DOI
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doi:10.1080/00036846.2013.851780
- Handle
- Last update
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10.03.2025, 11:45 AM CET
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
- Artikel
Associated
- Reichert, Arndt
- Tauchmann, Harald
- Taylor and Francis
- ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften, Leibniz-Informationszentrum Wirtschaft
Time of origin
- 2014