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Prognosen der regionalen Konjunkturentwicklung

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  • Christian Dreger
  • Konstantin A. Kholodilin

Abstract

German business cycle forecasts refer to the whole country. However, the usefulness of these forecasts at the regional level is not uncontroversial. Significant deviations between regional and national forecasts could occur if the sectoral structure is different. In this case, the forecast for the entire economy might be rather uninformative for a specific region. To bridge the gap, we develop a method for business cycle forecasts at the regional level which can be applied for the German states. In particular, the regional evolution is explained by the national economic development and regional indicators. The latter are determined by means of a principal component analysis from a huge set of time series. Using this approach, we are able to forecast the regional performance for the current year and the year ahead. The principal technique is illustrated for the state of Berlin. Vorhersagen der konjunkturellen Entwicklung werden in Deutschland meist für die Ebene der Gesamtwirtschaft durchgeführt. Sie sind jedoch nicht auf die regionalen Verhältnisse übertragbar. Signifikante Abweichungen können sich unter anderem wegen einer unterschiedlichen Sektoralstruktur ergeben. Eventuell liefert die gesamtwirtschaftliche Prognose ein verzerrtes Bild, wenn es darum geht, die künftige wirtschaftliche Entwicklung in der Region zu beurteilen. Daher wird hier ein Verfahren zur Prognose der regionalen Wirtschaftsentwicklung diskutiert, das auf der Ebene der einzelnen Bundesländer einsetzbar ist. Darin wird die regionale Entwicklung zum einen durch den gesamtwirtschaftlichen Verlauf erklärt. Zum anderen sind regionalspezifische Indikatoren entscheidend, die im Rahmen einer Hauptkomponentenanalyse bestimmt werden. Das Verfahren erlaubt eine verlässliche Vorhersage der regionalen Entwicklung in den einzelnen Bundesländern im laufenden und im folgenden Jahr. Die Methode wird exemplarisch für das Land Berlin dargestellt.

Suggested Citation

  • 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.
    • Handle: RePEc:diw:diwvjh:76-4-5
    DOI: 10.3790/vjh.76.4.47
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    1. Marcellino, Massimiliano & Banerjee, Anindya & Masten, Igor, 2005. "Forecasting macroeconomic variables for the new member states of the European Union," Working Paper Series 482, European Central Bank.
    2. Tommaso Proietti, 2006. "Temporal disaggregation by state space methods: Dynamic regression methods revisited," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 357-372, November.
    3. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
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    Cited by:

    1. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    2. 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).
    3. 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.
    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. Rüdiger Hamm, 2011. "Creative Class as a Determinant of Economic Development - Empirical Considerations for Northrhine-Westphalian Regions based on Time-Series Analysis," ERSA conference papers ersa11p828, European Regional Science Association.
    6. 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.
    7. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
    8. 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.
    9. Joachim Ragnitz & Stefan Arent & Wolfgang Nierhaus & Beate Schirwitz & Johannes Steinbrecher & Gerit Vogt & Björn Ziegenbalg, 2010. "Methodenexpertise zur Analyse der Auswirkungen der internationalen Finanz- und Wirtschaftskrise auf die Wirtschaft im Land Brandenburg : Gutachten im Auftrag des Ministeriums für Wirtschaft des Landes," ifo Dresden Studien, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 53, May.
    10. Robert Lehmann & Klaus Wohlrabe, 2012. "Die Prognose des Bruttoinlandsprodukts auf regionaler Ebene," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 65(21), pages 17-23, November.
    11. Beate Schirwitz & Christian Seiler & Klaus Wohlrabe, 2009. "Regionale Konjunkturzyklen in Deutschland – Teil I: Die Datenlage," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(13), pages 18-24, July.

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    More about this item

    Keywords

    Regional economy; forecasting; principal component analysis;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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