Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/72697
Authors: 
Kakamu, Kazuhiko
Polasek, Wolfgang
Year of Publication: 
2007
Series/Report no.: 
Reihe Ökonomie / Economics Series, Institut für Höhere Studien (IHS) 203
Abstract: 
We suggest a new class of cross-sectional space-time models based on local AR models and nearest neighbors using distances between observations. For the estimation we use a tightness prior for prediction of regional GDP forecasts. We extend the model to the model with exogenous variable model and hierarchical prior models. The approaches are demonstrated for a dynamic panel model for regional data in Central Europe. Finally, we find that an ARNN(1, 3) model with travel time data is best selected by marginal likelihood and there the spatial correlation is usually stronger than the time correlation.
Subjects: 
dynamic panel data
hierarchical models
marginal likelihoods
nearest neighbors
tightness prio
spatial econometrics
JEL: 
C11
C15
C21
R11
Document Type: 
Working Paper

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