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Do SVAR Models Justify Discarding the Technology Shock-Driven Real Business Cycle Hypothesis?

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  • Hyeon-seung Huh

    (Yonsei University, Republic of Korea)

  • David Kim

    (University of Sydney, Australia)

Abstract

This paper investigates the validity of technology shocks as a driving force of U.S. business cycle fluctuations. Using three well-known structural vector autoregression (SVAR) models, we analyze how structural shocks are associated with the variations of output and hours worked at business cycle frequencies. Empirical results reveal that technology shocks remain an important source of cyclical movements in output. Furthermore, a positive technology shock does not lead to a decline in hours worked in contrast to previous studies. Our SVARbased evidence does not support discarding a technology shock-driven business cycle theory.

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Bibliographic Info

Paper provided by Yonsei University, Yonsei Economics Research Institute in its series Working papers with number 2013rwp-59.

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Length: 33pages
Date of creation: Dec 2013
Date of revision:
Handle: RePEc:yon:wpaper:2013rwp-59

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Keywords: Structural vector autoregression; Technology shocks; Demand shocks; Real business cycles;

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References

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  1. Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
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  3. Adrian R. Pagan & M. Hashem Pesaran, 2008. "Econometric Analysis of Structural Systems with Permanent and Transitory Shocks," Discussion Papers 2008-04, School of Economics, The University of New South Wales.
  4. Dupasquier, Chantal & Guay, Alain & St-Amant, Pierre, 1999. "A Survey of Alternative Methodologies for Estimating Potential Output and the Output Gap," Journal of Macroeconomics, Elsevier, vol. 21(3), pages 577-595, July.
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  6. Chantal Dupasquier & Alain Guay & Pierre St-Amant, 1997. "A Comparison of Alternative Methodologies for Estimating Potential Output and the Output Gap," Working Papers 97-5, Bank of Canada.
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  9. Lawrence J. Christiano & Martin Eichenbaum & Robert J. Vigfusson, 2003. "The response of hours to a technology shock: evidence based on direct measures of technology," International Finance Discussion Papers 790, Board of Governors of the Federal Reserve System (U.S.).
  10. Fisher, Lance A. & Huh, Hyeon-Seung & Summers, Peter M., 2000. "Structural Identification of Permanent Shocks in VEC Models: A Generalization," Journal of Macroeconomics, Elsevier, vol. 22(1), pages 53-68, January.
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  12. Cogley, Timothy & Nason, James M, 1995. "Output Dynamics in Real-Business-Cycle Models," American Economic Review, American Economic Association, vol. 85(3), pages 492-511, June.
  13. Pengfei Wang & Yi Wen, 2011. "Understanding the Effects of Technology Shocks," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(4), pages 705-724, October.
  14. Fama, Eugene F., 1992. "Transitory variation in investment and output," Journal of Monetary Economics, Elsevier, vol. 30(3), pages 467-480, December.
  15. Jordi Galí & Pau Rabanal, 2004. "Technology Shocks and Aggregate Fluctuations," IMF Working Papers 04/234, International Monetary Fund.
  16. Cochrane, John H, 1994. "Permanent and Transitory Components of GNP and Stock Prices," The Quarterly Journal of Economics, MIT Press, vol. 109(1), pages 241-65, February.
  17. Jonas D. M. Fisher, 2006. "The Dynamic Effects of Neutral and Investment-Specific Technology Shocks," Journal of Political Economy, University of Chicago Press, vol. 114(3), pages 413-451, June.
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