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How fast can the new economy grow? A Bayesian analysis of the evolution of trend growth

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  • Cogley, Timothy

Abstract

This paper uses consumption data to estimate the trend growth rate for the “new economy.'' The analysis starts with the assumption that a trend break in GDP should be accompanied by a trend break in consumption. But because consumption is forward looking and smoother than GDP, it should be easier to detect a trend break in the former. The forward looking nature of consumption allows us to incorporate the private expectations of U.S. households about the new economy. The relative smoothness makes it easier to separate changes in trend growth from ordinary cyclical movements. The evidence confirms that there has been an increase in trend growth over the last 5 years, but the increase seems rather modest. The new economy is likely to grow more rapidly than in the 1970s, but not as fast as in the 1950s or early 1960s.
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  • Cogley, Timothy, 2005. "How fast can the new economy grow? A Bayesian analysis of the evolution of trend growth," Journal of Macroeconomics, Elsevier, vol. 27(2), pages 179-207, June.
  • Handle: RePEc:eee:jmacro:v:27:y:2005:i:2:p:179-207
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    Cited by:

    1. Eran Guse, 2004. "Expectational Business Cycles," Money Macro and Finance (MMF) Research Group Conference 2004 97, Money Macro and Finance Research Group.
    2. Bae, Jinho & Nelson, Charles R., 2007. "Earnings growth and the bull market of the 1990s: Is there a case for rational exuberance?," Journal of Macroeconomics, Elsevier, vol. 29(4), pages 690-707, December.
    3. Benati, Luca, 2007. "Drift and breaks in labor productivity," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2847-2877, August.
    4. Kahn, James A. & Rich, Robert W., 2007. "Tracking the new economy: Using growth theory to detect changes in trend productivity," Journal of Monetary Economics, Elsevier, vol. 54(6), pages 1670-1701, September.
    5. Eo, Yunjong & Morley, James C., 2008. "Likelihood-Based Confidence Sets for the Timing of Structural Breaks," MPRA Paper 10372, University Library of Munich, Germany.
    6. Chang-Jin Kim & Jeremy M. Piger & Richard Startz, 2007. "The Dynamic Relationship between Permanent and Transitory Components of U.S. Business Cycles," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(1), pages 187-204, February.
    7. Benati, Luca & Mumtaz, Haroon, 2007. "U.S. evolving macroeconomic dynamics: a structural investigation," Working Paper Series 746, European Central Bank.
    8. James B. Bullard & John Duffy, 2004. "Learning and structural change in macroeconomic data," Working Papers 2004-016, Federal Reserve Bank of St. Louis.
    9. Juan Antolin-Diaz & Thomas Drechsel & Ivan Petrella, 2017. "Tracking the Slowdown in Long-Run GDP Growth," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 343-356, May.
    10. Antolin-Diaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2014. "Following the Trend: Tracking GDP when Long-Run Growth is Uncertain," CEPR Discussion Papers 10272, C.E.P.R. Discussion Papers.
    11. Guse, Eran A., 2014. "Adaptive learning, endogenous uncertainty, and asymmetric dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 355-373.
    12. Attfield, Cliff & Temple, Jonathan R.W., 2010. "Balanced growth and the great ratios: New evidence for the US and UK," Journal of Macroeconomics, Elsevier, vol. 32(4), pages 937-956, December.
    13. C. Glocker & G. Sestieri & P. Towbin, 2017. "Time-varying fiscal spending multipliers in the UK," Working papers 643, Banque de France.
    14. Polbin, Andrey & Fokin, Nikita, 2017. "К Вопросу О Долгосрочной Взаимосвязи Реального Потребления Домохозяйств С Реальным Доходом В Рф
      [A note on cointegration relationship between real consumption and real income in Russia]
      ," MPRA Paper 82451, University Library of Munich, Germany, revised Nov 2017.
    15. Eran Guse, 2011. "Adaptive Learning, Endogenous Uncertainty, and Asymmetric Dynamics," Working Papers 11-01, Department of Economics, West Virginia University.
    16. Arratibel, Olga & Michaelis, Henrike, 2014. "The impact of monetary policy and exchange rate shocks in Poland: evidence from a time-varying VAR," Working Paper Series 1636, European Central Bank.
    17. Jyoti Rahman & David Stephan & Gene Tunny, 2009. "Estimating trends in Australia's productivity," Treasury Working Papers 2009-01, The Treasury, Australian Government, revised Feb 2009.
    18. Kim, Chang-Jin, 2008. "Markov-switching and the Beveridge-Nelson decomposition: Has US output persistence changed since 1984?," Journal of Econometrics, Elsevier, vol. 146(2), pages 227-240, October.
    19. Cliff L.F. Attfield & Jonathan R.W. Temple, 2003. "Measuring trend output: how useful are the Great Ratios?," Bristol Economics Discussion Papers 03/555, Department of Economics, University of Bristol, UK.
    20. Luigi Bocola & Nils Gornemann, 2013. "Risk, economic growth and the value of U.S. corporations," Working Papers 13-10, Federal Reserve Bank of Philadelphia.
    21. Marco Valerio Geraci & Jean-Yves Gnabo, 2015. "Measuring interconnectedness between financial institutions with Bayesian time-varying vector autoregressions," Working Papers ECARES 2015-51, ULB -- Universite Libre de Bruxelles.
    22. Benati, Luca & Goodhart, Charles, 2008. "Investigating time-variation in the marginal predictive power of the yield spread," Journal of Economic Dynamics and Control, Elsevier, vol. 32(4), pages 1236-1272, April.
    23. Marco Valerio Geraci & Jean-Yves Gnabo, 2015. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying VARS," Working Papers ECARES ECARES 2015-51, ULB -- Universite Libre de Bruxelles.
    24. Alexander Murray, 2017. "What Explains the Post-2004 U.S.Productivity Slowdown?," CSLS Research Reports 2017-05, Centre for the Study of Living Standards.
    25. Jonathan Temple & Cliff Attfield, 2004. "Measuring trend growth: how useful are the great ratios?," Money Macro and Finance (MMF) Research Group Conference 2003 101, Money Macro and Finance Research Group.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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