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Do Technology Shocks Drive Hours Up or Down?

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  • Barbara Rossi
  • Elena Pesavento

Abstract

This paper analyzes the robustness of the estimate of a positive productivity shock on hours to the presence of a possible unit root in hours. Estimations in levels or in first differences provide opposite conclusions. We rely on an agnostic procedure in which the researcher does not have to choose between a specification in levels or in first differences. The method uses alternative approximations based on local-to-unity asymptotic theory and allows the lead-time of the impulse response function to be a fixed fraction of the sample size. These devices provide better approximations in small samples and give confidence bands that have better coverage properties at medium and long horizons than existing methods. We find that a positive productivity shock has a negative effect on hours, as in Francis and Ramey (2001), but the effect is much more short-lived, and disappears after two quarters. The effect becomes positive at business cycle frequencies, as in Christiano et al. (2003)

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 North American Summer Meetings with number 96.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:nasm04:96

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Keywords: Technology shocks; persistence; impulse response functions; Real Business Cycle.;

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References

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  1. Graham Elliott & Thomas J. Rothenberg & James H. Stock, 1992. "Efficient Tests for an Autoregressive Unit Root," NBER Technical Working Papers 0130, National Bureau of Economic Research, Inc.
  2. 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.
  3. Serena Ng & Pierre Perron, 1997. "Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power," Boston College Working Papers in Economics 369, Boston College Department of Economics, revised 01 Sep 2000.
  4. Elliott, Graham & Jansson, Michael, 2000. "Testing for Unit Roots with Stationary Covariances," University of California at San Diego, Economics Working Paper Series qt47k7z69n, Department of Economics, UC San Diego.
  5. Kilian, Lutz & Chang, Pao-Li, 2000. "How accurate are confidence intervals for impulse responses in large VAR models?," Economics Letters, Elsevier, vol. 69(3), pages 299-307, December.
  6. Graham Elliott & Michael Jansson & Elena Pesavento, 2005. "Optimal Power for Testing Potential Cointegrating Vectors With Known Parameters for Nonstationarity," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 34-48, January.
  7. Elliott, Graham & Stock, James H., 2001. "Confidence intervals for autoregressive coefficients near one," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 155-181, July.
  8. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What happens after a technology shock?," International Finance Discussion Papers 768, Board of Governors of the Federal Reserve System (U.S.).
  9. Jordi Gali, 1996. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations," NBER Working Papers 5721, National Bureau of Economic Research, Inc.
  10. Neville Francis & Valerie A. Ramey, 2002. "Is the Technology-Driven Real Business Cycle Hypothesis Dead?," NBER Working Papers 8726, National Bureau of Economic Research, Inc.
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Citations

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Cited by:
  1. Luis Alberiko Gil-Alana & Antonio Moreno, . "Technology Shocks and Hours Worked: A Fractional Integration Perspective," Faculty Working Papers 03/06, School of Economics and Business Administration, University of Navarra.
  2. Patrick Fève & Alain Guay, 2007. "Identification of Technology Shocks in Structural VARs," Cahiers de recherche 0736, CIRPEE.
  3. Cristiano Cantore & Miguel A. Leon-Ledesma & Peter McAdam & Alpo Willman, 2013. "Shocking Stuff: Technology, Hours, and Factor Substitution," School of Economics Discussion Papers 0913, School of Economics, University of Surrey.
  4. Neville Francis & Michael T. Owyang & Jennifer E. Roush & Riccardo DiCecio, 2010. "A flexible finite-horizon alternative to long-run restrictions with an application to technology shock," Working Papers 2005-024, Federal Reserve Bank of St. Louis.
  5. Ghent, Andra, 2006. "Comparing Models of Macroeconomic Fluctuations: How Big Are the Differences?," MPRA Paper 180, University Library of Munich, Germany.
  6. Neville Francis & Michael T. Owyang & Jennifer E. Roush, 2005. "A flexible finite-horizon identification of technology shocks," International Finance Discussion Papers 832, Board of Governors of the Federal Reserve System (U.S.).

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