IDEAS home Printed from https://ideas.repec.org/p/sce/scecfa/287.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://repec.org/sce2006/up.18498.1141058846.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    4. repec:dgr:rugsom:01c05 is not listed on IDEAS
    5. 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.
    6. J. Paul Elhorst, 2003. "Specification and Estimation of Spatial Panel Data Models," International Regional Science Review, , vol. 26(3), pages 244-268, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Mark V. JANIKAS & Sergio J. REY, 2008. "On The Relationships Between Spatial Clustering, Inequality, And Economic Growth In The United States : 1969-2000," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 27, pages 13-34.
    3. Ana Angulo & Jesús Mur & Javier Trivez, 2014. "Measure of the resilience to Spanish economic crisis: the role of specialization," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 263-275.
    4. Ana Angulo & Jesús Mur & Javier Trívez, 2013. "Forecasting heterogeneous regional data: the case of European employment," ERSA conference papers ersa13p953, European Regional Science Association.
    5. Baltagi, Badi H. & Pirotte, Alain, 2014. "Prediction in a spatial nested error components panel data model," International Journal of Forecasting, Elsevier, vol. 30(3), pages 407-414.
    6. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2012. "Forecasting with spatial panel data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3381-3397.
    7. Ricardo Carvalho De Andrade Lima & Raul Da Mota Silveira Neto, 2016. "Physical and Human Capital and Brazilian Regional Growth: A Spatial Econometric Approach for the Period 1970–2010," Regional Studies, Taylor & Francis Journals, vol. 50(10), pages 1688-1701, October.
    8. Salima Bouayad-Agha & Nadine Turpin & Lionel Védrine, 2010. "Fostering the potential endogenous development of European regions: a spatial dynamic panel data analysis of the Cohesion Policy on regional convergence over the period 1980-2005," TEPP Working Paper 2010-17, TEPP.
    9. Kathleen P. Bell & Timothy J. Dalton, 2007. "Spatial Economic Analysis in Data‐Rich Environments," Journal of Agricultural Economics, Wiley Blackwell, vol. 58(3), pages 487-501, September.
    10. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.
    11. Yang, Zhenlin & Li, Chenwei & Tse, Y.K., 2006. "Functional form and spatial dependence in dynamic panels," Economics Letters, Elsevier, vol. 91(1), pages 138-145, April.
    12. Konstantin Arkadievich Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2008. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 195-207.
    13. Xiaowen Dai & Libin Jin, 2021. "Minimum distance quantile regression for spatial autoregressive panel data models with fixed effects," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-13, December.
    14. Fingleton, Bernard, 2010. "Predicting the geography of house prices," LSE Research Online Documents on Economics 33507, London School of Economics and Political Science, LSE Library.
    15. W. Saart, Patrick & Kim, Namhyun & Bateman, Ian, 2021. "Understanding spatial heterogeneity in GB agricultural land-use for improved policy targeting," Cardiff Economics Working Papers E2021/8, Cardiff University, Cardiff Business School, Economics Section.
    16. Badi H. Baltagi, 2008. "Forecasting with panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 153-173.
    17. Michael Artis & Christian Dreger & Konstantin Kholodilin, 2009. "Common and spatial drivers in regional business cycles," Centre for Growth and Business Cycle Research Discussion Paper Series 118, Economics, The University of Manchester.
    18. J. Paul Elhorst, 2014. "Matlab Software for Spatial Panels," International Regional Science Review, , vol. 37(3), pages 389-405, July.
    19. Mahmut ERDOGAN & Jülide YILDIRIM & Nadir ÖCAL, 2008. "Financial Development and Economic Growth in Turkey: a Spatial Effect Analysis," EcoMod2008 23800034, EcoMod.
    20. Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2019. "A time-space dynamic panel data model with spatial moving average errors," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 13-31.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sce:scecfa:287. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/sceeeea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.