Observation-driven models provide a flexible framework for modelling time series of counts. They are able to capture a wide range of dependence structures. Many applications in this field of research are concerned with count series whose conditional distribution given past observations and...

The paper makes three contributions to the economics literature on entrepreneurship. We offer a new measure of entrepreneurship which accounts for variations in persistence in self-employment. We outline an econometric methodology to account for this approach and find that it is superior to...

This paper compares various models for time series of counts which can account for discreetness, overdispersion and serial correlation. Besides observation- and parameter-driven models based upon corresponding conditional Poisson distributions, we also consider a dynamic ordered probit model as...

This paper extends the truncated and endogenously stratified Poisson and negative binomial models to three alternative discrete distributions, namely the generalized Poisson, geometric, and Borel distributions. Our primary intention here is to demonstrate how improper treatment of the data...

This paper is concerned with the analysis of zero-inflated count data when time of exposure varies. It proposes a new zero-inflated count data model that is based on two homogeneous Poisson processes and accounts for exposure time in a theory consistent way. The new model is used in an...