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Forecasting GDP at the regional level with many predictors

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  • Lehmann, Robert
  • Wohlrabe, Klaus

Abstract

In this paper, we assess the accuracy of macroeconomic forecasts at the regional level using a large data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden- Württemberg) and Eastern Germany. We overcome the problem of a ’data-poor environment’ at the sub-national level by complementing various regional indicators with more than 200 national and international indicators. We calculate single– indicator, multi–indicator, pooled and factor forecasts in a pseudo real–time setting. Our results show that we can significantly increase forecast accuracy compared to an autoregressive benchmark model, both for short and long term predictions. Furthermore, regional indicators play a crucial role for forecasting regional GDP.

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Bibliographic Info

Paper provided by University of Munich, Department of Economics in its series Discussion Papers in Economics with number 17104.

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Date of creation: 14 Sep 2013
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Handle: RePEc:lmu:muenec:17104

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Keywords: regional forecasting; forecast combination; factor models; model confidence set; data–rich environment;

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Citations

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Cited by:
  1. Robert Lehmann & Klaus Wohlrabe, 2013. "Sektorale Prognosen und deren Machbarkeit auf regionaler Ebene – Das Beispiel Sachsen," ifo Dresden berichtet, Ifo Institute for Economic Research at the University of Munich, vol. 20(04), pages 22-29, 08.
  2. 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).
  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. "Sectoral gross value-added forecasts at the regional level: Is there any information gain?," MPRA Paper 46765, University Library of Munich, Germany.
  5. Robert Lehmann & Klaus Wohlrabe, 2013. "Forecasting gross value-added at the regional level: Are sectoral disaggregated predictions superior to direct ones?," Ifo Working Paper Series Ifo Working Paper No. 171, Ifo Institute for Economic Research at the University of Munich.
  6. 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.
  7. 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.
  8. Christian Seiler & Klaus Wohlrabe, 2013. "Das ifo Geschäftsklima und die deutsche Konjunktur," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 66(18), pages 17-21, October.
  9. Robert Lehmann & Michael Weber, 2014. "Der Blick in die Glaskugel wird schärfer: Eine Evaluation der Treffsicherheit der ifo Dresden Konjunkturprognosen," ifo Dresden berichtet, Ifo Institute for Economic Research at the University of Munich, vol. 21(03), pages 45-46, 06.

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