The challenge of truckload routing is increased in complexity by the introduction of stochastic demand. Typically, this demand is generalized to follow a Poisson distribution. In this chapter, we cluster the demand data using data mining techniques to establish the more acceptable distribution...

This note looks at estimation of spatial autoregressive models for non-negative and count outcomes with multiplicative fixed effects. We show that in presence of significant proportion of zeros in the count variable, control function and Instrumental Variable estimation to model such spatial...

This note looks at estimation of count models with multiplicative fixed effects and spatial dependence in the form of spatial lag autocorrelation. We show that in presence of significant proportion of zeros in the count variable, control function estimation to model such spatial dependence is...

Count data regressions are an important tool for empirical analyses ranging from analyses of patent counts to measures of health and unemployment. Along with negative binomial, Poisson panel regressions are a preferred method of analysis because the Poisson conditional fixed effects maximum...

This paper explores differences in the frequency with which students from different schools reach high levels of math achievement. Data from the American Mathematics Competitions is used to produce counts of high-scoring students from more than two thousand public, coeducational, non-magnet,...

This paper focuses on developing and adapting statistical models of counts (non-negative integers) in the context of panel data and using them to analyze the relationship between patents and R&D expenditures. The model used is an application and generalization of the Poisson distribution to...

This work aims to identify the determinants of health care utilization in Portugal and to quantify their effects. A clear idea about this may help the policy debate on the delivery and organization of health services. Toward this end, we developed an approach based on count data models. We found...

Multivariate count models represent a natural way of accommodating data from multiple product categories when the dependent variable in each category is represented by a positive integer. In this paper, we propose a new simultaneous equation multi-category count data model ndash; the...

The purpose of this paper is to apply count data models to predict the number of times a credit applicant will not pay the amount awarded to repay the credit. Poisson models and negative binomial distribution models, taking into account the observed heterogeneity, are generally used in...