Showing 1 - 10 of 14
Persistent link: https://www.econbiz.de/10009159117
We propose a dynamic factor model for the analysis of multivariate time series count data. Our model allows for idiosyncratic as well as common serially correlated latent factors in order to account for potentially complex dynamic interdependence between series of counts. The model is estimated...
Persistent link: https://www.econbiz.de/10003738598
Persistent link: https://www.econbiz.de/10001627138
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...
Persistent link: https://www.econbiz.de/10002817440
Persistent link: https://www.econbiz.de/10013268717
In this paper we consider ML estimation for a broad class of parameter-driven models for discrete dependent variables with spatial correlation. Under this class of models, which includes spatial discrete choice models, spatial Tobit models and spatial count data models, the dependent variable is...
Persistent link: https://www.econbiz.de/10009685715
Persistent link: https://www.econbiz.de/10011694767
We develop a panel data count model combined with a latent Gaussian spatio-temporal heterogenous state process to analyze monthly severe crimes at the census tract level in Pittsburgh, Pennsylvania. Our data set combines Uniform Crime Reporting data with socio-economic data from the 2000 census....
Persistent link: https://www.econbiz.de/10014135197
The steadily growing access to high-quality spatio-temporal crime count data with a high level of spatial detail allows to uncover interesting relationships between crime types within and between small regional units. Data coherent forecasting of such counts has to take the integer and...
Persistent link: https://www.econbiz.de/10013238764
This paper proposes a new spatial lag regression model which addresses global spatial autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the spatial correlation parameter as a measurement of the impact of neighbouring...
Persistent link: https://www.econbiz.de/10013459466