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Regional Economic Forecasting: State-of-the-Art Methodology and Future Challenge

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  • Robert Lehmann

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  • Klaus Wohlrabe

    ()

Abstract

Over the last decade, the topic of regional economic forecasting has become increasingly prevalent in academic literature. The most striking problem in this context is data availability at a regional level. However, considerable methodological improvements have been made to address this problem. This paper summarizes a multitude of articles from academic journals and describes state-of-the-art techniques in regional economic forecasting. After identifying current practices, the article closes with a roadmap for possible future research activities.

Suggested Citation

  • Robert Lehmann & Klaus Wohlrabe, 2014. "Regional Economic Forecasting: State-of-the-Art Methodology and Future Challenge," CESifo Working Paper Series 5145, CESifo Group Munich.
  • Handle: RePEc:ces:ceswps:_5145
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    References listed on IDEAS

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    1. Matías Mayor & Roberto Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Working Paper series 15_12, Rimini Centre for Economic Analysis, revised Oct 2012.
    2. Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2008. "Neural networks and genetic algorithms as forecasting tools: a case study on German regions," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 35(4), pages 701-722, July.
    3. Rapach, David E. & Strauss, Jack K., 2012. "Forecasting US state-level employment growth: An amalgamation approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 315-327.
    4. Wenzel, Lars & Wolf, André, 2013. "Short-term forecasting with business surveys: Evidence for German IHK data at federal state level," HWWI Research Papers 140, Hamburg Institute of International Economics (HWWI).
    5. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    6. Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2008. "Neural networks and genetic algorithms as forecasting tools: a case study on German regions," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 35(4), pages 701-722, July.
    7. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    8. Jon R. Miller, 1998. "original: Spatial aggregation and regional economic forecasting," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 32(2), pages 253-266.
    9. Wolfgang Polasek & Richard Sellner & Wolfgang Schwarzbauer, 2007. "Long term regional forecasting with spatial equation systems," Working Paper series 10_07, Rimini Centre for Economic Analysis.
    10. Thirlwall, A. P., 1975. "Forecasting regional unemployment in Great Britain," Regional Science and Urban Economics, Elsevier, vol. 5(3), pages 357-374, August.
    11. Megna, Robert & Xu, Qiang, 2003. "Forecasting the New York State economy: The coincident and leading indicators approach," International Journal of Forecasting, Elsevier, vol. 19(4), pages 701-713.
    12. F. Javier TrÎvez & Jesßs Mur, 1999. "original: A short-term forecasting model for sectoral regional employment," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 33(1), pages 69-91.
    13. Patuelli, Roberto & Longhi, Simonetta & Reggiani, Aura & Nijkamp, Peter & Blien, Uwe, 2007. "A Rank-Order Test on the Statistical Performance of Neural Network Models for Regional Labor Market Forecasts," The Review of Regional Studies, Southern Regional Science Association, vol. 37(1), pages 64-81.
    14. Eric Girardin & Konstantin A. Kholodilin, 2011. "How helpful are spatial effects in forecasting the growth of Chinese provinces?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 622-643, November.
    15. Wenzel, Lars, 2013. "Forecasting regional growth in Germany: A panel approach using business survey data," HWWI Research Papers 133, Hamburg Institute of International Economics (HWWI).
    16. Simonetta Longhi & Peter Nijkamp & Aura Reggianni & Erich Maierhofer, 2005. "Neural Network Modeling as a Tool for Forecasting Regional Employment Patterns," International Regional Science Review, , vol. 28(3), pages 330-346, July.
    17. Robert Lehmann & Klaus Wohlrabe, 2014. "Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 34(1), pages 61-90, February.
    18. Kopoin, Alexandre & Moran, Kevin & Paré, Jean-Pierre, 2013. "Forecasting regional GDP with factor models: How useful are national and international data?," Economics Letters, Elsevier, vol. 121(2), pages 267-270.
    19. 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.
    20. J A Kurre & B R Weller, 1989. "Forecasting the Local Economy, Using Time-Series and Shift—Share Techniques," Environment and Planning A, , vol. 21(6), pages 753-770, June.
    21. Weller, Barry R & Kurre, James A, 1987. "Applicability of the Transfer Function Approach to Forecasting Employment Levels in Small Regions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 21(1), pages 34-43, March.
    22. Anil Puri & Gökçe Soydemir, 2000. "Forecasting industrial employment figures in Southern California: A Bayesian vector autoregressive model," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 34(4), pages 503-514.
    23. J A Kurre & B R Weller, 1989. "Forecasting the local economy, using time-series and shift - share techniques," Environment and Planning A, Pion Ltd, London, vol. 21(6), pages 753-770, June.
    24. Uwe Blien & Alexandros Tassinopoulos, 2001. "Forecasting Regional Employment with the ENTROP Method," Regional Studies, Taylor & Francis Journals, vol. 35(2), pages 113-124.
    25. Robert Lehmann & Klaus Wohlrabe, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, Verein für Socialpolitik, vol. 16(2), pages 226-254, May.
    26. Holmes, Richard A. & Shamsuddin, Abul F. M., 1993. "Evaluation of alternative leading indicators of British Columbia industrial employment," International Journal of Forecasting, Elsevier, vol. 9(1), pages 77-83, April.
    27. Carol Taylor West & Thomas M. Fullerton Jr., 2004. "Assessing the Historical Accuracy of Regional Economic Forecasts," Urban/Regional 0404009, University Library of Munich, Germany.
    28. Bandholz, Harm & Funke, Michael, 2003. "Die Konstruktion und Schätzung eines Konjunkturfrühindikators für Hamburg," Wirtschaftsdienst – Zeitschrift für Wirtschaftspolitik (1949 - 2007), ZBW – German National Library of Economics / Leibniz Information Centre for Economics, vol. 83(8), pages 540-548.
    29. Weller, Barry R., 1989. "National indicator series as quantitative predictors of small region monthly employment levels," International Journal of Forecasting, Elsevier, vol. 5(2), pages 241-247.
    30. Christian Dreger & Konstantin A. Kholodilin, 2007. "Prognosen der regionalen Konjunkturentwicklung," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 76(4), pages 47-55.
    31. Glennon, Dennis & Lane, Julia & Johnson, Stanley, 1987. "Regional econometric models that reflect labor market relations," International Journal of Forecasting, Elsevier, vol. 3(2), pages 299-312.
    32. repec:rim:rimwps:10-07 is not listed on IDEAS
    33. Coomes, Paul A., 1992. "A Kalman filter formulation for noisy regional job data," International Journal of Forecasting, Elsevier, vol. 7(4), pages 473-481, March.
    34. Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.
    35. Gary L. Shoesmith, 2000. "The Time-Series Relatedness of State and National Indexes of Leading Indicators and Implications for Regional Forecasting," International Regional Science Review, , vol. 23(3), pages 281-299, July.
    36. Matias Mayor & Ana Jesus Lopez & Rigoberto Perez, 2007. "Forecasting Regional Employment with Shift-Share and ARIMA Modelling," Regional Studies, Taylor & Francis Journals, vol. 41(4), pages 543-551.
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    Citations

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    Cited by:

    1. Steffen Henzel & Robert Lehmann & Klaus Wohlrabe, 2015. "Die Machbarkeit von Kurzfristprognosen für den Freistaat Sachsen," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 22(04), pages 21-25, August.
    2. repec:spr:lsprsc:v:10:y:2017:i:2:d:10.1007_s12076-016-0179-1 is not listed on IDEAS
    3. Henzel Steffen R. & Wohlrabe Klaus & Lehmann Robert, 2015. "Nowcasting Regional GDP: The Case of the Free State of Saxony," Review of Economics, De Gruyter, vol. 66(1), pages 71-98, April.
    4. Federico Lampis, 2016. "Forecasting the sectoral GVA of a small Spanish region," Economics and Business Letters, Oviedo University Press, vol. 5(2), pages 38-44.
    5. Robert Lehmann & Klaus Wohlrabe, 2017. "Boosting and regional economic forecasting: the case of Germany," Letters in Spatial and Resource Sciences, Springer, vol. 10(2), pages 161-175, July.
    6. Semerikova, Elena & Demidova, Olga, 2016. "Using spatial econometric models for regional unemployment forecasting," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 43, pages 29-51.

    More about this item

    Keywords

    regional economic forecasting; regional gross-domestic product; regional labour markets;

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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