Count Data Models with Viriance of Unknown Form - An Application to a Hedonic Model of Worker Absenteeism
AbstractWe examine an econometric model of counts of worker absences due to illness in a sluggishly adjusting hedonic labor market. We compare three estimators that parameterize the conditional variance--least squares, Poisson, and negative binomial pseudo maximum likelihood--to generalized least squares (GLS) using nonparametric estimates of the conditional variance. Our data support the hedonic absenteeism model. Semiparametric GLS coefficients are similar in sign, magnitude, and statistical significance to coefficients where the mean and variance of the errors are specified ex ante. In our data, coefficient estimates are sensitive to a regressor list but not to the econometric technique, including correcting for possible heteroskedasticity of unknown form. Â© 1997 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology
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Bibliographic InfoPaper provided by Indiana - Center for Econometric Model Research in its series Papers with number 94-011.
Length: 21 pages
Date of creation: 1994
Date of revision:
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Postal: Indiana University, Center for Econometric Model Research, Department of Economics; Bloomington, IN 47405.
Web page: http://www.indiana.edu/~econweb/
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labour market ; absenteeism;
Other versions of this item:
- Miguel A. Delgado & Thomas J. Kniesner, 1997. "Count Data Models With Variance Of Unknown Form: An Application To A Hedonic Model Of Worker Absenteeism," The Review of Economics and Statistics, MIT Press, vol. 79(1), pages 41-49, February.
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- Georges Dionne & Benoit Dostie, 2007. "New Evidence on the Determinants of Absenteeism Using Linked Employer-Employee Data," Industrial and Labor Relations Review, ILR Review, Cornell University, ILR School, vol. 61(1), pages 108-120, October.
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- Bradley, Steve & Green, Colin & Leeves, Gareth, 2007. "Worker absence and shirking: Evidence from matched teacher-school data," Labour Economics, Elsevier, vol. 14(3), pages 319-334, June.
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