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VAR-Prognose-Pooling : ein Ansatz zur Verbesserung der Informationsgrundlage der ifo Dresden Konjunkturprognosen

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  • Gerit Vogt

Abstract

Seit einigen Jahren werden von der Dresdner Niederlassung des ifo Instituts im halbjährlichen Rhythmus Prognosen zur gesamtwirtschaftlichen Entwicklung in Sachsen und Ostdeutschland erstellt. Das Bruttoinlandsprodukt (BIP), als wichtigste Konjunktur- und Prognosevariable, wird dabei entstehungsseitig aus der in den einzelnen Wirtschaftsbereichen erwarteten Bruttowertschöpfung berechnet. In diesem Beitrag wird ein Ansatz vorgestellt, der die Informationsgrundlage der ifo Dresden-Konjunkturprognosen verbessern soll. Er basiert auf der Kombination von Prognosen, die mit sparsam spezifizierten vektorautoregressiven Modellen (VAR-Modellen) generiert werden. Der Ansatz wird anhand der vierteljährlichen BIP-Daten vorgestellt, die jüngst vom ifo Institut für den Freistaat Sachsen vorgelegt wurden.

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Bibliographic Info

Article provided by Ifo Institute for Economic Research at the University of Munich in its journal ifo Dresden berichtet.

Volume (Year): 17 (2010)
Issue (Month): 02 (04)
Pages: 32-40

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Handle: RePEc:ces:ifodre:v:17:y:2010:i:02:p:32-40

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Related research

Keywords: Regionale Entwicklung; Prognoseverfahren; Konjunkturindikator; VAR-Modell; Sachsen; Neue Bundesländer;

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References

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  1. Johannes Mayr & Dirk Ulbricht, 2007. "VAR Model Averaging for Multi-Step Forecasting," Ifo Working Paper Series Ifo Working Paper No. 48, Ifo Institute for Economic Research at the University of Munich.
  2. Wolfgang Nierhaus, 2007. "Vierteljährliche volkswirtschaftliche Gesamtrechnungen für Sachsen mit Hilfe temporaler Disaggregation," ifo Dresden berichtet, Ifo Institute for Economic Research at the University of Munich, vol. 14(04), pages 24-36, 08.
  3. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
  4. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  5. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-89, June.
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Cited by:
  1. 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).
  2. Robert Lehmann & Klaus Wohlrabe, 2012. "Forecasting GDP at the Regional Level with Many Predictors," CESifo Working Paper Series 3956, CESifo Group Munich.
  3. 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.
  4. 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.

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