When outcome heterogeneously matters for selection
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Date
2012-09-27
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Abstract
The classical Heckman (1976, 1979) selection correction estimator (heckit) is
misspecified and inconsistent if an interaction of the outcome variable and an
explanatory variable matters for selection. To address this specification 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.
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Keywords
generalized estimator, heterogeneity, interaction, selection bias