When Outcome Heterogeneously Matters for Selection – A Generalized Selection Correction Estimator

16 Pages Posted: 31 Oct 2012

See all articles by Arndt R. Reichert

Arndt R. Reichert

World Bank; Rhine-Westphalia Institute for Economic Research (RWI-Essen)

Harald Tauchmann

Rhine-Westphalia Institute for Economic Research (RWI-Essen)

Date Written: October 10, 2012

Abstract

The classical Heckman (1976, 1979) selection correction estimator (heckit) is mis-specified and inconsistent if an interaction of the outcome variable and an explanatory variable matters for selection. To address this specifi cation problem, a full information maximum likelihood estimator and a simple two-step estimator are developed. Monte-Carlo simulations illustrate that the bias of the ordinary heckit estimator is removed by these generalized estimation procedures. Along with OLS and the ordinary heckit procedure, we apply these estimators to data from a randomized trial that evaluates the effectiveness of financial incentives for weight loss among the obese. Estimation results indicate that the choice of the estimation procedure clearly matters.

Keywords: selection bias, interaction, heterogeneity, generalized estimator

JEL Classification: C24, C93

Suggested Citation

Reichert, Arndt Rudiger and Tauchmann, Harald, When Outcome Heterogeneously Matters for Selection – A Generalized Selection Correction Estimator (October 10, 2012). Ruhr Economic Paper No. 372, Available at SSRN: https://ssrn.com/abstract=2159727

Arndt Rudiger Reichert

World Bank ( email )

1818 H Street, NW
Washington, DC 20433
United States

Rhine-Westphalia Institute for Economic Research (RWI-Essen) ( email )

Hohenzollernstr. 1-3
Essen, 45128
Germany

Harald Tauchmann (Contact Author)

Rhine-Westphalia Institute for Economic Research (RWI-Essen) ( email )

Hohenzollernstr. 1-3
45128 Essen
Germany

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