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Erscheinungsjahr: 
2007
Schriftenreihe/Nr.: 
Reihe Ökonomie / Economics Series No. 203
Verlag: 
Institute for Advanced Studies (IHS), Vienna
Zusammenfassung: 
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.
Schlagwörter: 
dynamic panel data
hierarchical models
marginal likelihoods
nearest neighbors
tightness prio
spatial econometrics
JEL: 
C11
C15
C21
R11
Dokumentart: 
Working Paper

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