Resum:
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This paper tries to resolve some of the main shortcomings in the empirical literature of location decisions
for new plants, i.e. spatial effects and overdispersion. Spatial effects are omnipresent, being
a source of overdispersion in the data as well as a factor shaping the functional relationship between
the variables that explain a firm’s location decisions. Using Count Data models, empirical
researchers have dealt with overdispersion and excess zeros by developments of the Poisson regression
model. This study aims to take this a step further, by adopting Bayesian methods and models
in order to tackle the excess of zeros, spatial and non-spatial overdispersion and spatial dependence
simultaneously. Data for Catalonia is used and location determinants are analysed to that end. The
results show that spatial effects are determinant. Additionally, overdispersion is descomposed into
an unstructured iid effect and a spatially structured effect.
Keywords: Bayesian Analysis, Spatial Models, Firm Location.
JEL Classification: C11, C21, R30. |