response-based sampling for binary choice models with sample selection

Clicks: 226
ID: 227409
2018
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Abstract
Sample selection models attempt to correct for non-randomly selected data in a two-model hierarchy where, on the first level, a binary selection equation determines whether a particular observation will be available for the second level (outcome equation). If the non-random selection mechanism induced by the selection equation is ignored, the coefficient estimates in the outcome equation may be severely biased. When the selection mechanism leads to many censored observations, few data are available for the estimation of the outcome equation parameters, giving rise to computational difficulties. In this context, the main reference is Greene (2008) who extends the results obtained by Manski and Lerman (1977), and develops an estimator which requires the knowledge of the true proportion of occurrences in the outcome equation. We develop a method that exploits the advantages of response-based sampling schemes in the context of binary response models with a sample selection, relaxing this assumption. Estimation is based on a weighted version of Heckman’s likelihood, where the weights take into account the sampling design. In a simulation study, we found that, for the outcome equation, the results obtained with our estimator are comparable to Greene’s in terms of mean square error. Moreover, in a real data application, it is preferable in terms of the percentage of correct predictions.
Reference Key
arezzo2018econometricsresponse-based Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Maria Felice Arezzo;Giuseppina Guagnano
Journal developmental cognitive neuroscience
Year 2018
DOI
10.3390/econometrics6010012
URL
http://www.mdpi.com/2225-1146/6/1/12
Keywords
selection bias observational study

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