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Demographics and Business Cycle Volatility: A Spurious Relationship?*

* This paper is a replication of an original study

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  • Gerdie Everaert
  • Hauke Vierke

Abstract

This paper replicates the estimation results of three studies on the impact of the age composition of the labor force on business cycle volatility and investigates whether they signal a meaningful long-run relationship. We show that both the volatile-age labor force share variable and the business cycle volatility measure exhibit non-stationary behavior but find no robust evidence of cointegration. Hence, the estimation results reported in the literature may be spurious. This conclusion is further supported by the finding that the strong relationship (i) disappears when cross-sectional dependence is accounted for using the CCEP estimator and (ii) is highly sensitive to small changes in the composition of the sample, to data revisions, and to the exact definition of the volatile-age labor share.
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Suggested Citation

  • Gerdie Everaert & Hauke Vierke, 2016. "Demographics and Business Cycle Volatility: A Spurious Relationship?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1467-1477, November.
    • Handle: RePEc:wly:japmet:v:31:y:2016:i:7:p:1467-1477
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    References listed on IDEAS

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    1. Nir Jaimovich & Henry E. Siu, 2009. "The Young, the Old, and the Restless: Demographics and Business Cycle Volatility," American Economic Review, American Economic Association, vol. 99(3), pages 804-826, June.
    2. Nicolau, João, 2002. "Stationary Processes That Look Like Random Walks— The Bounded Random Walk Process In Discrete And Continuous Time," Econometric Theory, Cambridge University Press, vol. 18(1), pages 99-118, February.
    3. Everaert, Gerdie, 2014. "A panel analysis of the fisher effect with an unobserved I(1) world real interest rate," Economic Modelling, Elsevier, vol. 41(C), pages 198-210.
    4. Kapetanios, G. & Pesaran, M. Hashem & Yamagata, T., 2011. "Panels with non-stationary multifactor error structures," Journal of Econometrics, Elsevier, vol. 160(2), pages 326-348, February.
    5. Ai Deng, 2014. "Understanding Spurious Regression in Financial Economics," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 122-150.
    6. 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.
    7. Antonio E. Noriega & Daniel Ventosa‐Santaulària, 2007. "Spurious Regression and Trending Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(3), pages 439-444, June.
    8. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    9. Mark Gertler & Kenneth Rogoff, 2003. "NBER Macroeconomics Annual 2002, Volume 17," NBER Books, National Bureau of Economic Research, Inc, number gert03-1, February.
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    12. Antonio E. Noriega & Daniel Ventosa‐Santaulària, 2006. "Spurious Regression Under Broken‐Trend Stationarity," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 671-684, September.
    13. Karlsson, Sune & Lothgren, Mickael, 2000. "On the power and interpretation of panel unit root tests," Economics Letters, Elsevier, vol. 66(3), pages 249-255, March.
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    16. repec:bla:obuest:v:61:y:1999:i:0:p:631-52 is not listed on IDEAS
    17. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    18. Peter C. B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Econometrica, Econometric Society, vol. 67(5), pages 1057-1112, September.
    19. Nir Jaimovich & Seth Pruitt & Henry E. Siu, 2013. "The Demand for Youth: Explaining Age Differences in the Volatility of Hours," American Economic Review, American Economic Association, vol. 103(7), pages 3022-3044, December.
    20. Jean‐Pierre Urbain & Joakim Westerlund, 2011. "Least Squares Asymptotics in Spurious and Cointegrated Panel Regressions with Common and Idiosyncratic Stochastic Trends," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(1), pages 119-139, February.
    21. Chu Ba & Kozhan Roman, 2010. "Spurious Regressions of Stationary AR(p) Processes with Structural Breaks," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(1), pages 1-25, December.
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    Cited by:

    1. Martin Iseringhausen & Hauke Vierke, 2019. "What Drives Output Volatility? The Role of Demographics and Government Size Revisited," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(4), pages 849-867, August.
    2. Lorenzo Carbonari & Vincenzo Atella & Paola Samà, 2018. "Hours worked in selected OECD countries: an empirical assessment," International Review of Applied Economics, Taylor & Francis Journals, vol. 32(4), pages 525-545, July.
    3. Mario Holzner & Stefan Jestl & David Pichler, 2022. "Public and private pension systems and macroeconomic volatility in OECD countries," Scottish Journal of Political Economy, Scottish Economic Society, vol. 69(2), pages 131-168, May.
    4. Milanez Ana, 2020. "Workforce Ageing and Labour Productivity Dynamics," Naše gospodarstvo/Our economy, Sciendo, vol. 66(3), pages 1-13, September.
    5. George Kapetanios & Laura Serlenga & Yongcheol Shin, 2023. "Testing for correlation between the regressors and factor loadings in heterogeneous panels with interactive effects," Empirical Economics, Springer, vol. 64(6), pages 2611-2659, June.

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    Replication

    This item is a replication of:
  • Nir Jaimovich & Henry E. Siu, 2009. "The Young, the Old, and the Restless: Demographics and Business Cycle Volatility," American Economic Review, American Economic Association, vol. 99(3), pages 804-826, June.

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