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

Kakamu, Kazuhiko and Polasek, Wolfgang (February 2007) Cross-sectional Space-time Modeling Using ARNN(p, n) Processes. Former Series > Working Paper Series > IHS Economics Series 203


Download (272kB) | Preview


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.;

Item Type: IHS Series
Keywords: 'Dynamic panel data' 'Hierarchical models' 'Marginal likelihoods' 'Nearest neighbors' 'Tightness prio' 'Spatial econometrics'
Classification Codes (e.g. JEL): C11, C15, C21, R11
Date Deposited: 26 Sep 2014 10:38
Last Modified: 27 Sep 2019 06:31
ISBN: 1605-7996

Actions (login required)

View Item View Item