Abstract
The econometric issue of sample selection concerns the possible biases that arise when a nonrandomly sampled set of observations from a population is used as if the sample were random to make inferences about that population. Current literature, with a few exceptions noted below, has focused on, and finely tuned, the known results relating to this issue in the framework of the linear regression model and analysis of a continuous dependent variable, such as hours worked or wages. This paper will examine an extension of the sample selection model to the Poisson regression model for discrete, count data, such as numbers of patents, of children, of visits to facilities such as shops or recreation sites, of convictions for crimes committed, and so on.
Helpful comments of Rainer Winkelmann and participants in department seminars at Amherst College, Washington University, and the University of Umea are gratefully acknowledged.
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Greene, W. (2001). Fiml Estimation of Sample Selection Models for Count Data. In: Negishi, T., Ramachandran, R.V., Mino, K. (eds) Economic Theory, Dynamics and Markets. Research Monographs in Japan-U.S. Business & Economics, vol 5. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1677-4_6
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DOI: https://doi.org/10.1007/978-1-4615-1677-4_6
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