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Activity Number: 530 - SPEED: Survey Research Methods
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 11:15 AM
Sponsor: Survey Research Methods Section
Abstract #325208
Title: The Heckman Selection Model with Complex Survey Data
Author(s): Michael Machiorlatti* and Sixia Chen and Dr. Sara Vesely
Companies: University of Oklahoma Health Sciences Center and and The University of Oklahoma Health Sciences Center
Keywords: Heckman ; Survey ; Weighting ; Sampling
Abstract:

The Heckman selection model proposed by Heckman (1979) has been used extensively in economics to correct selection bias. It is closely related to nonignorable nonresponse problems. In previous literature, inference based on the Heckman selection model with complex survey data has not been rigorously studied. Results from the Heckman model by assuming simple random sampling can be biased and misleading. In this paper, we rigorously studied theoretical properties of estimates from Heckman model by incorporating sampling design features. In addition, we propose an efficient weight smoothing approach to further improve efficiency of the estimates under informative sampling. Simulation studies assess the impact of this approach and show the benefits of our proposed methods. Empirical study has been done by using a real data application.


Authors who are presenting talks have a * after their name.

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