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An unconventional weekly economic activity index for Germany

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  • Eraslan, Sercan
  • Götz, Thomas

Abstract

We develop an unconventional activity index for the German economy at weekly frequency in order to monitor economic developments at the current end in real time. The index contains nine high-frequency, rather unconventional weekly indicators. These are augmented by monthly industrial production and quarterly gross domestic product. The weekly activity index is then calculated as the common factor of the mixed-frequency dataset. It turns out that the index exhibits a high correlation with quarterly GDP growth and is thus able to serve as a reliable weekly coincident indicator for economic activity in Germany.

Suggested Citation

  • Eraslan, Sercan & Götz, Thomas, 2020. "An unconventional weekly economic activity index for Germany," Technical Papers 02/2020, Deutsche Bundesbank.
    • Handle: RePEc:zbw:bubtps:283322
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    1. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    2. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    3. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    4. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
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    1. Grebe, Moritz & Kandemir, Sinem & Tillmann, Peter, 2024. "Uncertainty about the war in Ukraine: Measurement and effects on the German economy," Journal of Economic Behavior & Organization, Elsevier, vol. 217(C), pages 493-506.

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