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The Response of Hours to a Technology Shock: a Two-Step Structural VAR Approach


  • Patrick Fève
  • Alain Guay


The response of hours worked to a technology shock is an important and a controversial issue in macroeconomics. Unfortunately, the estimated response is generally sensitive to the specification of hours in SVARs. This paper uses a simple two-step approach in order to consistently estimate technology shocks from a SVAR model and the response of hours that follow this shock. The first step considers a SVAR model with a set of relevant stationary variables, but excluding hours. Given a consistent estimate of technology shocks in the first step, the response of hours to this shock is estimated in a second step. When applied to US data, the two-step approach predicts a short-run decrease of hours after a technology improvement followed by a hump-shaped positive response. This result is robust to the specification of hours, different sample periods, measures of hours and output and to the variables included in the VAR in the first step.

Suggested Citation

  • Patrick Fève & Alain Guay, 2007. "The Response of Hours to a Technology Shock: a Two-Step Structural VAR Approach," Cahiers de recherche 0737, CIRPEE.
  • Handle: RePEc:lvl:lacicr:0737

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    References listed on IDEAS

    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," Review of Economic Studies, Oxford University Press, vol. 61(4), pages 631-653.
    3. 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.
    4. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2004. "A Critique of Structural VARs Using Real Business Cycle Theory," Levine's Bibliography 122247000000000518, UCLA Department of Economics.
    5. 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.
    6. 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.
    7. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What Happens After a Technology Shock?," NBER Working Papers 9819, National Bureau of Economic Research, Inc.
    8. Jordi Gali Garreta & 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.
    9. Jordi Gali Garreta & 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.
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    Cited by:

    1. Marcos Sanso-Navarro, 2011. "Broken trend stationarity of hours worked," Post-Print hal-00712742, HAL.
    2. Charles, Amélie & Darné, Olivier & Tripier, Fabien, 2015. "Are Unit Root Tests Useful In The Debate Over The (Non)Stationarity Of Hours Worked?," Macroeconomic Dynamics, Cambridge University Press, vol. 19(01), pages 167-188, January.
    3. Fabrice Collard & Patrick Fève, 2012. "Sur les causes et les effets en macro économie : les Contributions de Sargent et Sims, Prix Nobel d'Economie 2011," Revue d'économie politique, Dalloz, vol. 122(3), pages 335-364.
    4. Francesco Furlanetto & Martin Seneca, 2012. "Rule-of-Thumb Consumers, Productivity, and Hours," Scandinavian Journal of Economics, Wiley Blackwell, vol. 114(2), pages 658-679, June.
    5. Andrei Polbin & Sergey Drobyshevsky, 2014. "Developing a Dynamic Stochastic Model of General Equilibrium for the Russian Economy," Research Paper Series, Gaidar Institute for Economic Policy, issue 166P, pages 156-156.

    More about this item


    SVARs; long-run restriction; technology shocks; consumption to output ratio; hours worked;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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