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Aggregation of Space-Time Processes

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Author Info
Raffaella Giacomini
Clive W.J. Granger

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Abstract

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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Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number 2001-07.

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Date of creation: May 2001
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Handle: RePEc:cdl:ucsdec:2001-07

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  1. Kohn, Robert, 1982. "When is an aggregate of a time series efficiently forecast by its past?," Journal of Econometrics, Elsevier, vol. 18(3), pages 337-349, April. [Downloadable!] (restricted)
  2. Tobias, Justin & Zellner, Arnold, 2004. "A Note on Aggregation, Disaggregation and Forecasting Performance," Staff General Research Papers 12024, Iowa State University, Department of Economics.
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  3. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1986. "Forecasting and conditional projection using realistic prior distribution," Staff Report 93, Federal Reserve Bank of Minneapolis. [Downloadable!]
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  4. Elhorst, J.P., 2000. "Dynamic models in space and time," Research Report 00C16, University of Groningen, Research Institute SOM (Systems, Organisations and Management). [Downloadable!]
  5. Pesaran, M Hashem & Pierse, Richard G & Kumar, Mohan S, 1989. "Econometric Analysis of Aggregation in the Context of Linear Prediction Models," Econometrica, Econometric Society, vol. 57(4), pages 861-88, July. [Downloadable!] (restricted)
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  6. Pinkse, Joris & Slade, Margaret E., 1998. "Contracting in space: An application of spatial statistics to discrete-choice models," Journal of Econometrics, Elsevier, vol. 85(1), pages 125-154, July. [Downloadable!] (restricted)
  7. 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. [Downloadable!] (restricted)
  8. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October. [Downloadable!] (restricted)
  9. Baillie, Richard T., 1980. "Predictions from ARMAX models," Journal of Econometrics, Elsevier, vol. 12(3), pages 365-374, April. [Downloadable!] (restricted)
  10. Patrick E. Brown & Gareth O. Roberts & Kjetil F. Kåresen & Stefano Tonellato, 2000. "Blur-generated non-separable space-time models," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 847-860. [Downloadable!] (restricted)
  11. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer, vol. 21(1), pages 243-247, December. [Downloadable!] (restricted)
  12. Hall, Alastair R, 1994. "Testing for a Unit Root in Time Series with Pretest Data-Based Model Selection," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 461-70, October.
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  1. Frédérick Demers & Annie De Champlain, 2005. "Forecasting Core Inflation in Canada: Should We Forecast the Aggregate or the Components?," Working Papers 05-44, Bank of Canada. [Downloadable!]
  2. Maximilian Auffhammer & Richard Carson & Teresa Garin-Munoz, 2004. "Forecasting China's Carbon Dioxide Emissions: A Provincial Approach," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series 971, Department of Agricultural & Resource Economics, UC Berkeley. [Downloadable!]
  3. Frédérick Demers & David Dupuis, 2005. "Forecasting Canadian GDP: Region-Specific versus Countrywide Information," Working Papers 05-31, Bank of Canada. [Downloadable!]
  4. Juan de Dios Tena & Antoni Espasa & Gabriel Pino, 2008. "Forecasting Spanish inflation using information from different sectors and geographical areas," Statistics and Econometrics Working Papers ws080101, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
  5. Kamarianakis, Yiannis & Prastacos, Poulicos, 2002. "Space-time modeling of traffic flow," ERSA conference papers ersa02p141, European Regional Science Association. [Downloadable!]
  6. Hampel, Katharina & Kunz, Marcus & Schanne, Norbert & Wapler, Rüdiger & Weyh, Antje, 2007. "Regional employment forecasts with spatial interdependencies," IAB Discussion Paper 200702, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]. [Downloadable!]
  7. Arnab Bhattacharjee & Chris Jensen-Butler, 2005. "Estimation of Spatial Weights Matrix in a Spatial Error Model, with an Application to Diffusion in Housing Demand," CRIEFF Discussion Papers 0519, Centre for Research into Industry, Enterprise, Finance and the Firm. [Downloadable!]
  8. Arnab Bhattacharjee & Chris Jensen-Butler, 2005. "A Model of Regional Housing Markets in England and Wales," CRIEFF Discussion Papers 0508, Centre for Research into Industry, Enterprise, Finance and the Firm. [Downloadable!]
  9. Rubén Hernández-Murillo & Michael T. Owyang, 2004. "The information content of regional employment data for forecasting aggregate conditions," Working Papers 2004-005, Federal Reserve Bank of St. Louis. [Downloadable!]
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  10. Caporin Massimiliano & Paruolo Paolo, 2005. "Spatial effects in multivariate ARCH," Economics and Quantitative Methods qf0501, Department of Economics, University of Insubria. [Downloadable!]
  11. Badi H. Baltagi, 2007. "Forecasting with Panel Data," Center for Policy Research Working Papers 91, Center for Policy Research, Maxwell School, Syracuse University. [Downloadable!]
    Other versions:
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