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A Dynamic Panel Data Approach to the Forecasting of the GDP of German L�nder

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  • Konstantin Arkadievich Kholodilin
  • Boriss Siliverstovs
  • Stefan Kooths

Abstract

Abstract In this paper, we make multi-step forecasts of the annual growth rates of the real GDP for each of the 16 German L�nder simultaneously. We apply dynamic panel models accounting for spatial dependence between regional GDP. We find that both pooling and accounting for spatial effects help to improve the forecast performance substantially. We demonstrate that the effect of accounting for spatial dependence is more pronounced for longer forecasting horizons (the forecast accuracy gain is about 9% for a 1-year horizon and exceeds 40% for a 5-year horizon). We recommend incorporating a spatial dependence structure into regional forecasting models, especially when long-term forecasts are made.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Spatial Economic Analysis.

Volume (Year): 3 (2008)
Issue (Month): 2 ()
Pages: 195-207

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Handle: RePEc:taf:specan:v:3:y:2008:i:2:p:195-207

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Related research

Keywords: German L�nder; forecasting; dynamic panel model; spatial autocorrelation; C21; C23; C53;

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References

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  1. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
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  4. Stefan Bach & Dieter Vesper, 2000. "Finanzpolitik und Wiedervereinigung: Bilanz nach 10 Jahren," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 69(2), pages 194-224.
  5. Longhi, Simonetta & Nijkamp, Peter, 2006. "Forecasting regional labor market developments under spatial heterogeneity and spatial correlation," Serie Research Memoranda 0015, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  6. Brücker, Herbert & Siliverstovs, Boriss, 2005. "On the Estimation and Forecasting of International Migration: How Relevant Is Heterogeneity Across Countries?," IZA Discussion Papers 1710, Institute for the Study of Labor (IZA).
  7. Hinze, Jörg, 2003. "Prognoseleistung von Frühindikatoren : Die Bedeutung von Frühindikatoren für Konjunkturprognosen - Eine Analyse für Deutschland," HWWA Discussion Papers 236, Hamburg Institute of International Economics (HWWA).
  8. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  9. Badi H. Baltagi & Georges Bresson & James M. Griffin & Alain Pirotte, 2003. "Homogeneous, heterogeneous or shrinkage estimators? Some empirical evidence from French regional gasoline consumption," Empirical Economics, Springer, vol. 28(4), pages 795-811, November.
  10. Stefan Mittnik & Peter A. Zadrozny, 2004. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly IFO Business Conditions Data," CESifo Working Paper Series 1203, CESifo Group Munich.
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  12. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  13. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  14. Badi H. Baltagi & James M. Griffin & Weiwen Xiong, 2000. "To Pool Or Not To Pool: Homogeneous Versus Hetergeneous Estimations Applied to Cigarette Demand," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 117-126, February.
  15. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2006. "The Use of Spatial Filtering Techniques: The Spatial and Space-time Structure of German Unemployment Data," Tinbergen Institute Discussion Papers 06-049/3, Tinbergen Institute.
  16. Erich Langmantel, 1999. "Das ifo Geschäftsklima als Indikator für die Prognose des Bruttoinlandsprodukts," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 52(16-17), pages 16-21, October.
  17. Baltagi, Badi H. & Griffin, James M., 1997. "Pooled estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline," Journal of Econometrics, Elsevier, vol. 77(2), pages 303-327, April.
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  19. Konstantin A. Kholodilin & Boriss Siliverstovs, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 226(3), pages 234-259, May.
  20. Badi H. Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: The Case of Liquor," Center for Policy Research Working Papers 84, Center for Policy Research, Maxwell School, Syracuse University.
  21. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2002. "Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators: Some empirical evidence from US electricity and natural-gas consumption," Economics Letters, Elsevier, vol. 76(3), pages 375-382, August.
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Cited by:
  1. Robert Lehmann & Klaus Wohlrabe, 2012. "Die Prognose des Bruttoinlandsprodukts auf regionaler Ebene," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 65(21), pages 17-23, November.
  2. Gerit Vogt, 2009. "Konjunkturprognose in Deutschland. Ein Beitrag zur Prognose der gesamtwirtschaftlichen Entwicklung auf Bundes- und Länderebene," ifo Beiträge zur Wirtschaftsforschung, Ifo Institute for Economic Research at the University of Munich, number 36, 8.
  3. 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).
  4. Lehmann, Robert & Wohlrabe, Klaus, 2013. "Forecasting GDP at the regional level with many predictors," Discussion Papers in Economics 17104, University of Munich, Department of Economics.
  5. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2012. "Estimating and Forecasting With A Dynamic Spatial Panel Data Model," Center for Policy Research Working Papers 149, Center for Policy Research, Maxwell School, Syracuse University.
  6. Konstantin A. Kholodilin & Andreas Mense, 2012. "Forecasting the Prices and Rents for Flats in Large German Cities," Discussion Papers of DIW Berlin 1207, DIW Berlin, German Institute for Economic Research.
  7. Ana Angulo & F. Trívez, 2010. "The impact of spatial elements on the forecasting of Spanish labour series," Journal of Geographical Systems, Springer, vol. 12(2), pages 155-174, June.
  8. Matías Mayor & Roberto Patuelli, 2013. "Spatial Panel Data Forecasting over Different Horizons, Cross-Sectional and Temporal Dimensions," ERSA conference papers ersa13p815, European Regional Science Association.
  9. 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).
  10. Hampel, Katharina & Kunz, Marcus & Schanne, Norbert & Wapler, Rüdiger & Weyh, Antje, 2007. "Regional employment forecasts with spatial interdependencies," IAB Discussion Paper 200702, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  11. Robert Lehmann & Klaus Wohlrabe, 2014. "Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?," Jahrbuch für Regionalwissenschaft, Springer, vol. 34(1), pages 61-90, February.
  12. Lehmann, Robert & Wohlrabe, Klaus, 2013. "Sectoral gross value-added forecasts at the regional level: Is there any information gain?," MPRA Paper 46765, University Library of Munich, Germany.
  13. J. Elhorst, 2012. "Dynamic spatial panels: models, methods, and inferences," Journal of Geographical Systems, Springer, vol. 14(1), pages 5-28, January.
  14. Jean-Sauveur Ay & Raja Chakir & Julie Le Gallo, 2014. "The effects of scale, space and time on the predictive accuracy of land use models," Working Papers 2014/02, INRA, Economie Publique.
  15. Matías Mayor & Roberto Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Working Paper Series 15_12, The Rimini Centre for Economic Analysis, revised Oct 2012.
  16. 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 Lande," ifo Dresden Studien, Ifo Institute for Economic Research at the University of Munich, number 53, October.

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