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Hours worked - Productivity puzzle: identification in fractional integration settings

  • Lovcha, Yuliya
  • Pérez Laborda, Àlex

A recent finding of the structural VAR literature is that the response of hours worked to a technology shock depends on the assumption on the order of integration of the hours. In this work we relax this assumption, allowing for fractional integration and long memory in the process for hours and productivity. We find that the sign and magnitude of the estimated impulse responses of hours to a positive technology shock depend crucially on the assumptions applied to identify them. Responses estimated with short-run identification are positive and statistically significant in all datasets analyzed. Long-run identification results in negative often not statistically significant responses. We check validity of these assumptions with the Sims (1989) procedure, concluding that both types of assumptions are appropriate to recover the impulse responses of hours in a fractionally integrated VAR. However, the application of longrun identification results in a substantial increase of the sampling uncertainty. JEL Classification numbers: C22, E32. Keywords: technology shock, fractional integration, hours worked, structural VAR, identification

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File URL: http://hdl.handle.net/2072/211796
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Paper provided by Universitat Rovira i Virgili, Department of Economics in its series Working Papers with number 2072/211796.

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Date of creation: 2013
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Handle: RePEc:urv:wpaper:2072/211796
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  1. Christopher J. Erceg & Luca Guerrieri & Christopher Gust, 2005. "Can Long-Run Restrictions Identify Technology Shocks?," Journal of the European Economic Association, MIT Press, vol. 3(6), pages 1237-1278, December.
  2. Faust, Jon & Leeper, Eric M, 1997. "When Do Long-Run Identifying Restrictions Give Reliable Results?," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 345-53, July.
  3. 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.
  4. Tschernig, Rolf & Weber, Enzo & Weigand, Roland, 2010. "Long-run Identification in a Fractionally Integrated System," University of Regensburg Working Papers in Business, Economics and Management Information Systems 447, University of Regensburg, Department of Economics.
  5. 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.
  6. Olivier Jean Blanchard & Danny Quah, 1988. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," NBER Working Papers 2737, National Bureau of Economic Research, Inc.
  7. Hosoya, Yuzo, 1996. "The quasi-likelihood approach to statistical inference on multiple time-series with long-range dependence," Journal of Econometrics, Elsevier, vol. 73(1), pages 217-236, July.
  8. 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.
  9. Christopher A. Sims, 1989. "Models and their uses," Discussion Paper / Institute for Empirical Macroeconomics 11, Federal Reserve Bank of Minneapolis.
  10. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2006. "Assessing structural VARs," International Finance Discussion Papers 866, Board of Governors of the Federal Reserve System (U.S.).
    • 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.
  11. Ray, Bonnie K., 1993. "Long-range forecasting of IBM product revenues using a seasonal fractionally differenced ARMA model," International Journal of Forecasting, Elsevier, vol. 9(2), pages 255-269, August.
  12. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
  13. Fabio Canova & David López-Salido & Claudio Michelacci, 2007. "The labor market effects of technology shocks," Banco de Espa�a Working Papers 0719, Banco de Espa�a.
  14. 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.
  15. 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.).
  16. Jeremy Berkowitz & Francis X. Diebold, 1998. "Bootstrapping Multivariate Spectra," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 664-666, November.
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