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
Englisch

Bibliographic citation
Journal: Applied Economics ; ISSN: 1466-4283 ; Volume: 46 ; Year: 2014 ; Issue: 7 ; Pages: 762-768 ; Milton Park, Abingdon: Taylor and Francis

Classification
Wirtschaft
Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
Field Experiments
Subject
selection bias
interaction
heterogeneity
generalized estimator

Event
Geistige Schöpfung
(who)
Reichert, Arndt
Tauchmann, Harald
Event
Veröffentlichung
(who)
Taylor and Francis
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften, Leibniz-Informationszentrum Wirtschaft
(where)
Milton Park, Abingdon
(when)
2014

DOI
doi:10.1080/00036846.2013.851780
Handle
Last update
10.03.2025, 11:45 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Reichert, Arndt
  • Tauchmann, Harald
  • Taylor and Francis
  • ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften, Leibniz-Informationszentrum Wirtschaft

Time of origin

  • 2014

Other Objects (12)