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

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Author Info
Kakamu, Kazuhiko (Graduate School of Economics, Osaka University, Osaka, Japan)
Polasek, Wolfgang (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria)

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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.

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File URL: http://www.ihs.ac.at/publications/eco/es-203.pdf
File Format: application/pdf
File Function: First version, 2007
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Publisher Info
Paper provided by Institute for Advanced Studies in its series Economics Series with number 203.

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Length: 25 pages
Date of creation: Feb 2007
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Handle: RePEc:ihs:ihsesp:203

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Related research
Keywords: Dynamic panel data; hierarchical models; marginal likelihoods; nearest neighbors; tightness prio; spatial econometrics;

Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
R11 - Urban, Rural, and Regional Economics - - General Regional Economics - - - Analysis of Growth, Development, and Changes

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This page was last updated on 2009-11-27.

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