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The Decline in German Output Volatility: A Bayesian Analysis

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  • Liesenfeld, Roman
  • Hogrefe, Jens
  • Aßmann, Christian

Abstract

Empirical evidence suggests a sharp volatility decline of the growth in U.S. gross domestic product (GDP) in the mid-1980s. Using Bayesian methods, we analyze whether a volatility reduction can also be detected for the German GDP. Since statistical inference for volatility processes critically depends on the specification of the conditional mean we assume for our volatility analysis different time series models for GDP growth. We find across all specifications evidence for an output stabilization around 1993, after the downturn following the boom associated with the German reunification. However, the different GDP models lead to alternative characterizations of this stabilization : In a linear AR model it shows up as smaller shocks hitting the economy, while regime switching models reveal as further sources for a stabilization, a narrowing gap between growth rates during booms and recessions or flatter trajectories characterizing the GDP growth rates. Furthermore, it appears that the reunification interrupted an output stabilization emerging already around 1987.

Suggested Citation

  • Liesenfeld, Roman & Hogrefe, Jens & Aßmann, Christian, 2005. "The Decline in German Output Volatility: A Bayesian Analysis," Economics Working Papers 2006-02, Christian-Albrechts-University of Kiel, Department of Economics.
  • Handle: RePEc:zbw:cauewp:4134
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    1. Ulrich Fritsche & Vladimir Kuzin, 2005. "Declining output volatility in Germany: impulses, propagation, and the role of monetary policy," Applied Economics, Taylor & Francis Journals, vol. 37(21), pages 2445-2457.
    2. Olivier Blanchard & John Simon, 2001. "The Long and Large Decline in U.S. Output Volatility," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 32(1), pages 135-174.
    3. Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
    4. David N. DeJong & Roman Liesenfeld & Jean-Francois Richard, 2006. "Timing structural change: a conditional probabilistic approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 175-190.
    5. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    6. James H. Stock & Mark W. Watson, 2005. "Understanding Changes In International Business Cycle Dynamics," Journal of the European Economic Association, MIT Press, vol. 3(5), pages 968-1006, September.
    7. Marcelle Chauvet & Simon Potter, 2001. "Recent Changes in the US Business Cycle," Manchester School, University of Manchester, vol. 69(5), pages 481-508, October.
    8. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    9. Hansen, Bruce E, 1997. "Approximate Asymptotic P Values for Structural-Change Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 60-67, January.
    10. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
    11. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    12. James H. Stock & Mark W. Watson, 2003. "Has the Business Cycle Changed and Why?," NBER Chapters, in: NBER Macroeconomics Annual 2002, Volume 17, pages 159-230, National Bureau of Economic Research, Inc.
    13. Kim, Chang-Jin & Nelson, Charles R & Piger, Jeremy, 2004. "The Less-Volatile U.S. Economy: A Bayesian Investigation of Timing, Breadth, and Potential Explanations," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 80-93, January.
    14. Ploberger, Werner & Kramer, Walter, 1992. "The CUSUM Test with OLS Residuals," Econometrica, Econometric Society, vol. 60(2), pages 271-285, March.
    15. Claudia M. Buch & Joerg Doepke & Christian Pierdzioch, 2004. "Business Cycle Volatility in Germany," German Economic Review, Verein für Socialpolitik, vol. 5(4), pages 451-479, November.
    16. Terence C. Mills & Ping Wang, 2003. "Have output growth rates stabilised? evidence from the g‐7 economies," Scottish Journal of Political Economy, Scottish Economic Society, vol. 50(3), pages 232-246, August.
    17. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    18. Michael Artis & Massimiliano Marcellino & Tommaso Proietti, 2004. "Dating Business Cycles: A Methodological Contribution with an Application to the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(4), pages 537-565, September.
    19. Zeileis, Achim & Kleiber, Christian & Kramer, Walter & Hornik, Kurt, 2003. "Testing and dating of structural changes in practice," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 109-123, October.
    20. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 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), De Gruyter, vol. 226(3), pages 234-259, June.
    21. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    22. Gerhard Bry & Charlotte Boschan, 1971. "Foreword to "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs"," NBER Chapters, in: Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, pages -1, National Bureau of Economic Research, Inc.
    23. Chang-Jin Kim & Charles R. Nelson, 1999. "Has The U.S. Economy Become More Stable? A Bayesian Approach Based On A Markov-Switching Model Of The Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 608-616, November.
    24. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1, March.
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    Cited by:

    1. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    2. Sandra Bilek-Steindl, 2012. "On the Change in the Austrian Business Cycle," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2012(1), pages 1-18.
    3. Claudia Buch & Martin Schlotter, 2013. "Regional origins of employment volatility: evidence from German states," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 40(1), pages 1-19, February.
    4. Magnus Reif, 2022. "Time‐Varying Dynamics of the German Business Cycle: A Comprehensive Investigation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(1), pages 80-102, February.
    5. Strotmann, Harald & Döpke, Jörg & Buch, Claudia M., 2006. "Does trade openness increase firm-level volatility?," Discussion Paper Series 1: Economic Studies 2006,40, Deutsche Bundesbank.
    6. Buch Claudia M & Doepke Joerg & Stahn Kerstin, 2009. "Great Moderation at the Firm Level? Unconditional vs. Conditional Output Volatility," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 9(1), pages 1-27, May.
    7. Hogrefe, Jens, 2007. "The yield spread and GDP growth - Time Varying Leading Properties and the Role of Monetary Policy," Economics Working Papers 2007-12, Christian-Albrechts-University of Kiel, Department of Economics.
    8. Konstantin A. Kholodilin & Erik Klär, 2007. "Dem Konjunkturzyklus auf der Spur: zur Prognose konjunktureller Wendepunkte in Deutschland," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 76(4), pages 8-20.

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    More about this item

    Keywords

    business cycle models; Gibbs sampling; Markov Chain Monte Carlo; regime switching; structural breaks;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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