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

Author

Listed:
  • Kakamu, Kazuhiko

    (Graduate School of Economics, Osaka University, Osaka, Japan)

  • Polasek, Wolfgang

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria)

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.

Suggested Citation

  • Kakamu, Kazuhiko & Polasek, Wolfgang, 2007. "Cross-sectional Space-time Modeling Using ARNN(p, n) Processes," Economics Series 203, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:203
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    File URL: https://irihs.ihs.ac.at/id/eprint/1752
    File Function: First version, 2007
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    More about this item

    Keywords

    Dynamic panel data; hierarchical models; marginal likelihoods; nearest neighbors; tightness prio; spatial econometrics;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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