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Aggregation of space-time processes

  • Giacomini, Raffaella
  • Granger, Clive W. J.

In this paper we compare the relative efficiency of different methods of forecasting the aggregate of spatially correlated variables. Small sample simulations confirm the asymptotic result that improved forecasting performance can be obtained by imposing a priori constraints on the amount of spatial correlation in the system. One way to do so is to aggregate forecasts from a Space-Time Autoregressive model (Cliff et al., 1975), which offers a solution to the 'curse of dimensionality' that arises when forecasting with VARs. We also show that ignoring spatial correlation, even when it is weak, leads to highly inaccurate forecasts. Finally, if the system satisfies a 'poolability' condition, there is a benefit in forecasting the aggregate variable directly.

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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 118 (2004)
Issue (Month): 1-2 ()
Pages: 7-26

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Handle: RePEc:eee:econom:v:118:y:2004:i:1-2:p:7-26
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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  2. F Stetzer, 1982. "Specifying weights in spatial forecasting models: the results of some experiments," Environment and Planning A, Pion Ltd, London, vol. 14(5), pages 571-584, May.
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  10. Aigner, Dennis J & Goldfeld, Stephen M, 1974. "Estimation and Prediction from Aggregate Data when Aggregates are Measured More Accurately than Their Components," Econometrica, Econometric Society, vol. 42(1), pages 113-34, January.
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  12. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer, vol. 21(1), pages 243-247, December.
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