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Do Technology Shocks Drive Hours Up or Down? A Little Evidence From an Agnostic Procedure

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  • Elena Pesavento

    (Emory University)

  • Barbara Rossi

    (Duke University)

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. We find that a positive productivity shock has a negative impact 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), although it is not significant.

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File URL: http://128.118.178.162/eps/em/papers/0411/0411002.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0411002.

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Length: 22 pages
Date of creation: 01 Nov 2004
Date of revision:
Handle: RePEc:wpa:wuwpem:0411002

Note: Type of Document - pdf; pages: 22
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Web page: http://128.118.178.162

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

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References

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  1. 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.
  2. Stock, James H., 1991. "Confidence intervals for the largest autoregressive root in U.S. macroeconomic time series," Journal of Monetary Economics, Elsevier, vol. 28(3), pages 435-459, December.
  3. 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.
  4. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-36, July.
  5. 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.).
  6. Graham Elliott & Michael Jansson & Elena Pesavento, 2003. "Optimal Power For Testing Potential Cointegrating Vectors with Known Parameters for Nonstationarity," Emory Economics 0303, Department of Economics, Emory University (Atlanta).
  7. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
  8. Elliott, Graham & Jansson, Michael, 2002. "Testing for Unit Roots with Stationary Covariates," University of California at San Diego, Economics Working Paper Series qt4v35s2gv, Department of Economics, UC San Diego.
  9. 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.
  10. 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.
  11. 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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Cited by:
  1. Jordi Gali & Pau Rabanal, 2004. "Technology Shocks and Aggregate Fluctuations: How Well Does the RBS Model Fit Postwar U.S. Data?," NBER Working Papers 10636, National Bureau of Economic Research, Inc.
  2. Morten O. Ravn & Saverio Simonelli, 2007. "Labor Market Dynamics and the Business Cycle: Structural Evidence for the United States," CSEF Working Papers 182, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
  3. Ulrich K. Müller & Mark W. Watson, 2008. "Testing Models of Low-Frequency Variability," Econometrica, Econometric Society, vol. 76(5), pages 979-1016, 09.
  4. GOSPODINOV, Nikolay & MAYNARD, Alex & PESAVENTO, Elena, 2009. "Sensitivity of Impulse Responses to Small Low Frequency Co-Movements : Reconciling the Evidence on the Effects of Technology Shocks," Cahiers de recherche 03-2009, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  5. Jordi Galí & Pau Rabanal, 2004. "Technology Shocks and Aggregate Fluctuations," IMF Working Papers 04/234, International Monetary Fund.
  6. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2012. "Persistence and Cycles in US Hours Worked," Discussion Papers of DIW Berlin 1200, DIW Berlin, German Institute for Economic Research.
  7. Ghent, Andra C., 2009. "Comparing DSGE-VAR forecasting models: How big are the differences?," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 864-882, April.

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