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Stock Markets and Business Cycle Comovement in Germany Before World War I: Evidence from Spectral Analysis


  • Ritschl, Albrecht
  • Uebele, Martin


This paper examines the comovement of the stock market and of real activity in Germany before World War I under the efficient market hypothesis. We employ multivariate spectral analysis to compare rivaling national product estimates to stock market behaviour in the frequency domain. Close comovement of one series with the stock market enables us to decide between various rivaling business cycle chronologies. We find that business cycle dates obtained from deflated national product series are severely distorted by interference with the implicit price deflator. Among the nominal series, the income estimate of Hoffmann (1965) correlates best with the stock market, while the tax based estimate of Hoffmann and Müller (1959) is too smooth especially before 1890. We find impressive comovement between the stock market and nominal wages, a sub-series of Hoffmann's income estimate. We can show that a substantial part of this nominal wage series is driven by data on real investment activity. Our findings confirm the traditional business cycle chronology for Germany of Burns and Mitchell (1946), and lead us to discard later attempts to date the business cycle.

Suggested Citation

  • Ritschl, Albrecht & Uebele, Martin, 2005. "Stock Markets and Business Cycle Comovement in Germany Before World War I: Evidence from Spectral Analysis," CEPR Discussion Papers 5370, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:5370

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    References listed on IDEAS

    1. Michele Boldrin & Lawrence J. Christiano & Jonas D.M. Fisher, 1995. "Asset Pricing Lessons for Modeling Business Cycles," NBER Working Papers 5262, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Allen, William A. & Moessner, Richhild, 2012. "The international propagation of the financial crisis of 2008 and a comparison with 1931," Financial History Review, Cambridge University Press, vol. 19(02), pages 123-147, August.
    2. Rania Jammazi & Chaker Aloui, 2014. "Cyclical components and dual long memory in the foreign exchange rate dynamics: the Tunisian case," Working Papers 2014-198, Department of Research, Ipag Business School.
    3. repec:psc:journl:v:9:y:2017:i:3:p:201-241 is not listed on IDEAS
    4. Sarferaz, Samad & Uebele, Martin, 2009. "Tracking down the business cycle: A dynamic factor model for Germany 1820-1913," Explorations in Economic History, Elsevier, vol. 46(3), pages 368-387, July.
    5. Thomas C. Owen, 2013. "Measuring business cycles in the Russian Empire," Economic History Review, Economic History Society, vol. 66(3), pages 895-916, August.
    6. Tang, Ling & Yu, Lean & He, Kaijian, 2014. "A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 128(C), pages 1-14.
    7. Lukasz Lenart, 2015. "Discrete Spectral Analysis. The Case of Industrial Production in Selected European Countries," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 27-47.
    8. Reijnders, Jan P.G., 2009. "Trend movements and inverted Kondratieff waves in the Dutch economy, 1800-1913," Structural Change and Economic Dynamics, Elsevier, vol. 20(2), pages 90-113, June.

    More about this item


    business cycle chronology; efficient market hypothesis; Imperial Germany; spectral analysis;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • N13 - Economic History - - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations - - - Europe: Pre-1913

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