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Sensitivity of Impulse Responses to Small Low-Frequency Comovements: Reconciling the Evidence on the Effects of Technology Shocks

  • Gospodinov, Nikolay
  • Maynard, Alex
  • Pesavento, Elena

This paper clarifies the empirical source of the debate on the effect of technology shocks on hours worked. We find that the contrasting conclusions from levels and differenced VAR specifications can be explained by a small, but important, low frequency co-movement between hours worked and labour productivity growth, which is allowed for in the levels specification but is implicitly set to zero in the differenced VAR. Our theoretical analysis shows that, even when the root of hours is very close to one and the low frequency co-movement is quite small, assuming away or explicitly removing the low frequency component can have large implications for the long-run identifying restrictions, giving rise to biases large enough to account for the empirical difference between the two specifications.

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File URL: http://pubs.amstat.org/doi/abs/10.1198/jbes.2011.10042
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Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 29 (2011)
Issue (Month): 4 ()
Pages: 455-467

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Handle: RePEc:bes:jnlbes:v:29:i:4:y:2011:p:455-467
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  1. Susanto Basu & John Fernald & Miles Kimball, 2004. "Are technology improvements contractionary?," Working Paper Series WP-04-20, Federal Reserve Bank of Chicago.
  2. Michelle Alexopoulos, 2011. "Read All about It!! What Happens Following a Technology Shock?," American Economic Review, American Economic Association, vol. 101(4), pages 1144-79, June.
  3. Francis, Neville & Ramey, Valerie A., 2005. "Is the technology-driven real business cycle hypothesis dead? Shocks and aggregate fluctuations revisited," Journal of Monetary Economics, Elsevier, vol. 52(8), pages 1379-1399, November.
  4. Federico Ravenna, 2006. "Vector autoregressions and reduced form representations of DSGE models," Banco de Espa�a Working Papers 0619, Banco de Espa�a.
  5. Yongsung Chang & Jay H. Hong, 2005. "Do technological improvements in the manufacturing sector raise or lower employment?," Working Papers 05-5, Federal Reserve Bank of Philadelphia.
  6. Harald Uhlig, 2004. "Do Technology Shocks Lead to a Fall in Total Hours Worked?," Journal of the European Economic Association, MIT Press, vol. 2(2-3), pages 361-371, 04/05.
  7. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-73, September.
  8. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What Happens After a Technology Shock?," NBER Working Papers 9819, National Bureau of Economic Research, Inc.
  9. Chang, Yongsung & Hornstein, Andreas & Sarte, Pierre-Daniel, 2009. "On the employment effects of productivity shocks: The role of inventories, demand elasticity, and sticky prices," Journal of Monetary Economics, Elsevier, vol. 56(3), pages 328-343, April.
  10. Jordi Galí & Pau Rabanal, 2004. "Technology Shocks and Aggregate Fluctuations: How Well Does the RBC Model Fit Postwar U.S. Data?," IMF Working Papers 04/234, International Monetary Fund.
  11. Pesavento, Elena & Rossi, Barbara, 2005. "Do Technology Shocks Drive Hours Up Or Down? A Little Evidence From An Agnostic Procedure," Macroeconomic Dynamics, Cambridge University Press, vol. 9(04), pages 478-488, September.
  12. Gali, J., 1996. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," Working Papers 96-28, C.V. Starr Center for Applied Economics, New York University.
  13. John Shea, 1999. "What Do Technology Shocks Do?," NBER Chapters, in: NBER Macroeconomics Annual 1998, volume 13, pages 275-322 National Bureau of Economic Research, Inc.
  14. Gospodinov, Nikolay, 2010. "Inference in Nearly Nonstationary SVAR Models With Long-Run Identifying Restrictions," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 1-12.
  15. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2007. "Assessing Structural VARs," NBER Chapters, in: NBER Macroeconomics Annual 2006, Volume 21, pages 1-106 National Bureau of Economic Research, Inc.
  16. Barbara Rossi (Duke) & Elena Pesavento (Emory), 2004. "Small sample confidence intervals for multivariate impulse response functions at long horizons," Econometric Society 2004 North American Winter Meetings 364, Econometric Society.
  17. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2008. "Are Structural VARs with Long-Run Restrictions Useful in Developing Business Cycle Theory?," NBER Working Papers 14430, National Bureau of Economic Research, Inc.
  18. Neville Francis & Michael T. Owyang & Athena T. Theodorou, 2003. "The use of long-run restrictions for the identification of technology shocks," Working Papers 2003-010, Federal Reserve Bank of St. Louis.
  19. Neville Francis & Michael T. Owyang & Jennifer E. Roush & Riccardo DiCecio, 2014. "A Flexible Finite-Horizon Alternative to Long-Run Restrictions with an Application to Technology Shocks," The Review of Economics and Statistics, MIT Press, vol. 96(3), pages 638-647, October.
  20. Fernald, John G., 2007. "Trend breaks, long-run restrictions, and contractionary technology improvements," Journal of Monetary Economics, Elsevier, vol. 54(8), pages 2467-2485, November.
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