Browsing by Person "Glaser, Stephanie"

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A review of spatial econometric models for count data
(2017) Glaser, Stephanie
Despite the increasing availability of spatial count data in research areas like technology spillovers, patenting activities, insurance payments, and crime forecasting, specialized models for analysing such data have received little attention in econometric literature so far. The few existing approaches can be broadly classified into observation-driven models, where the random spatial effects enter the moments of the dependent variable directly, and parameterdriven models, where the random spatial effects are unobservable and induced via a latent process. Moreover, within these groups the modelling approaches (and therefore the interpretation) of spatial effects are quite heterogeneous, stemming in part from the nonlinear structure of count data models. The purpose of this survey is to compare and contrast the various approaches for econometric modelling of spatial counts discussed in the literature.
Modelling and diagnostics of spatially autocorrelated counts
(2022) Jung, Robert C.; Glaser, Stephanie
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 observations on the conditional expectation of the counts. It allows for flexible likelihood-based inference based on different distributional assumptions using standard numerical procedures. In addition, we advocate the use of data-coherent diagnostic tools in spatial count regression models. The application revisits a data set on the location choice of single unit start-up firms in the manufacturing industry in the US.
Modelling of spatial effects in count data
(2017) Glaser, Stephanie; Jung, Robert
In this thesis, spatial structures in discrete valued count observations are modelled. More precisely, a global spatial autocorrelation parameter is estimated in the framework of a nonlinear count data regression model. For this purpose, cross-sectional and panel count data models are developed which incorporate spatial autocorrelation and allow for additional explanatory variables. The proposed models include the so-called "Spatial linear feedback model" for cross-sectional data as well as for panel data including fixed effects, which is estimated using maximum likelihood estimation. Additionally, two approaches for a distribution-free panel estimation using GMM are presented. The models are applied to a cross-sectional U.S. start-up firm births data set and a panel data set with crime counts from Pittsburgh.