Abstract
This paper discusses statistical models for count data in a unifying framework. In a typical econometric analysis one would assume that the count data one observes have been generated by some parametric distribution for non-negative integers p(y; θ), like for instance the Poisson, geometric, or negative binomial. Individual observed heterogeneity is introduced by letting the population parameter θ depend on observable individual characteristics x i , in general via some function of a linear predictor x i ß. This implies a specific conditional meanfunction (regression) E(Y|x;ß) = µ(x,ß), where the objective is then to estimate and draw inference on the ß’s (and possibly some additional parameters). A straightforward way ofestimation is by the method of maximum likelihood. This approach requires, however, the specification of a “true” probability model. The choice of a wrong model may yield inconsistent and inefficient estimates.
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© 1992 Springer-Verlag Berlin Heidelberg
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Winkelmann, R., Zimmermann, K.F. (1992). Recursive Probability Estimators for Count Data. In: Haag, G., Mueller, U., Troitzsch, K.G. (eds) Economic Evolution and Demographic Change. Lecture Notes in Economics and Mathematical Systems, vol 395. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-48808-5_17
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DOI: https://doi.org/10.1007/978-3-642-48808-5_17
Publisher Name: Springer, Berlin, Heidelberg
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