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

Author

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  • Riccardo Corradini

    (Università degli studi di Roma- la Sapienza)

Abstract

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 (www.istat.it) 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

Suggested Citation

  • Riccardo Corradini, 2006. "Advanced estimates of regional accounts: an alternative approach by spatial panels," Computing in Economics and Finance 2006 287, Society for Computational Economics.
    • Handle: RePEc:sce:scecfa:287
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    References listed on IDEAS

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    1. repec:dgr:rugsom:01c05 is not listed on IDEAS
    2. 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).
    3. Badi H. Baltagi & Dong Li, 2004. "Prediction in the Panel Data Model with Spatial Correlation," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 13, pages 283-295, Springer.
    4. Giuseppe Arbia & Gianfranco Piras, 2004. "Convergence in per-capita GDP across European regions using panel data models extended to spatial autocorrelation effects," ERSA conference papers ersa04p524, European Regional Science Association.
    5. J. Paul Elhorst, 2003. "Specification and Estimation of Spatial Panel Data Models," International Regional Science Review, , vol. 26(3), pages 244-268, July.
    6. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
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    More about this item

    Keywords

    spatial panel data models; regional accounts;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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