Non parametric checks for count data models : and application to demand for health care in Spain
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<subfield code="a">Non parametric checks for count data models</subfield>
<subfield code="b">: and application to demand for health care in Spain</subfield>
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<subfield code="a">This paper presents model specification checking procedures for count data regression models which are consistent in the direction of nonparametric alternatives. The discussion is motivated in the context of a model of demand for health care in Spain. The parameters of the regression model are estimated by maximun likelihood based on Poisson and Negative Binomial specifications as well as by ordinary least squares and semiparametric generalized least squeares. The test statistics are based on marked empirical processes which are not distribution free, but their critical values are well approximated by bootstrap. Such test are valid for testing the functional form of the conditional mean and conditional probabilities resulting from alternative distributional specifications. In our health care demand model, the linear exponential regression model with a Negative Binomial seems to be appropiate for this data set.</subfield>
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