When Outcome Heterogeneously matters for Selection: A Generalized Selection Correction Estimator
Reichert A, Tauchmann H (2014)
Publication Language: English
Publication Type: Other publication type
Publication year: 2014
Journal
Pages Range: 372
ISBN: 978-3-86788-427-3
DOI: 10.4419/8678842
Abstract
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.
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Involved external institutions
Germany (DE)How to cite
APA:
Reichert, A., & Tauchmann, H. (2014). When Outcome Heterogeneously matters for Selection: A Generalized Selection Correction Estimator.
MLA:
Reichert, Arndt, and Harald Tauchmann. When Outcome Heterogeneously matters for Selection: A Generalized Selection Correction Estimator. 2014.
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