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Do Technology Shocks Drive Hours Up or Down?

  • Barbara Rossi
  • Elena Pesavento

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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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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  1. Galí, Jordi, 1996. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," CEPR Discussion Papers 1499, C.E.P.R. Discussion Papers.
  2. Graham Elliott & Michael Jansson, . "Testing for Unit Roots with Stationary Covariates," Economics Working Papers 2000-6, Department of Economics and Business Economics, Aarhus University.
  3. Barbara Rossi & Elena Pesavento, 2006. "Small-sample confidence intervals for multivariate impulse response functions at long horizons," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1135-1155.
  4. 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).
  5. 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.
  6. Elliott, Graham & STOCK, JAMES H, 2000. "Confidence Intervals for Autoregressive Coefficients Near One," University of California at San Diego, Economics Working Paper Series qt6ww3p59v, Department of Economics, UC San Diego.
  7. 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.
  8. 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.
  9. 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.
  10. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What Happens After a Technology Shock?," NBER Working Papers 9819, National Bureau of Economic Research, Inc.
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