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Advanced estimates of regional accounts: an alternative approach by spatial panels


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  • Riccardo Corradini

    (Università degli studi di Roma- la Sapienza)


The policies related to regional economic activity developed by European Union (EU) and the role played by regions as economic subject have determined a bigger set of disaggregated statistics at macroeconomic level. The methodologies used nowadays by the Italian national institute of statistics (ISTAT) are based on an information set build on the basis of inner statistical surveys and other external sources. The estimates of regional accounts carried out on the complete information set require an amount of time bigger than the one expected for the already mentioned aims. A strong need to carry out advanced estimates of regional accounts in a quicker time has emerged. The Kalman filter could be the right tool if we use a short time series span. Since it is available a larger data set from ISTAT web site ( from 1980 up to 2004, a different approach will be performed here, and is mainly based on Spatial Panel recently used by Elhorst and Baltagi. SAR (simultaneous autocorrelation model) and SEM (simultaneous error model) will be used. In a similar fashion the first log differences of ULA (units of labour) will be used to forecast the first log differences of four value added branches at constant prices. Finally some conclusions will be drawn on the performances of SAR and SEM

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 287.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:287

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Keywords: spatial panel data models; regional accounts;

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  1. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, 9.
  2. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, Taylor & Francis Journals, vol. 1(2), pages 175-185.
  3. Giuseppe Arbia & Gianfranco Piras, 2005. "Convergence in per-capita GDP across European regions using panel data models extended to spatial autocorrelation effects," ISAE Working Papers, ISTAT - Italian National Institute of Statistics - (Rome, ITALY) 51, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
  4. Elhorst, J. Paul, 2001. "Panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable," Research Report 01C05, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
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