Cross-sectional Space-time Modeling Using ARNN(p, n) Processes

Kazuhiko Kakamu and Wolfgang Polasek ()
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Kazuhiko Kakamu: Graduate School of Economics, Osaka University, Osaka, Japan

No 203, Economics Series from Institute for Advanced Studies

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.

Keywords: Dynamic panel data; hierarchical models; marginal likelihoods; nearest neighbors; tightness prio; spatial econometrics (search for similar items in EconPapers)
JEL-codes: C11 C15 C21 R11 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-geo and nep-ure
Date: 2007-02
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