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The Effects of Permanent Technology Shocks on Labour Productivity and Hours in the RBC Model

  • Lindé, Jesper

Recent work on the effects of permanent technology shocks argue that the basic RBC model cannot account for a negative correlation between hours worked and labour productivity. In this Paper, I show that this conjecture is not necessarily correct. In the basic RBC model, I find that hours worked fall and labour productivity rises after a positive permanent technology shock once one allows for the possibility that the process for the permanent technology shock is persistent in growth rates. A more serious limitation of the RBC model is its inability to generate a persistent rise in hours worked after a positive permanent technology shock along with a rise in labour productivity that are in line with what the data suggests.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 4827.

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Date of creation: Jan 2005
Date of revision:
Handle: RePEc:cpr:ceprdp:4827
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  1. Gali, J., 1996. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," Working Papers 96-28, C.V. Starr Center for Applied Economics, New York University.
  2. Jonas Fisher, 2004. "Technology Shocks Matter," Econometric Society 2004 North American Winter Meetings 14, Econometric Society.
  3. Rotemberg, Julio J & Woodford, Michael, 1996. "Real-Business-Cycle Models and the Forecastable Movements in Output, Hours, and Consumption," American Economic Review, American Economic Association, vol. 86(1), pages 71-89, March.
  4. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What Happens After a Technology Shock?," NBER Working Papers 9819, National Bureau of Economic Research, Inc.
  5. Hansen, Bruce E., 1995. "Rethinking the Univariate Approach to Unit Root Testing: Using Covariates to Increase Power," Econometric Theory, Cambridge University Press, vol. 11(05), pages 1148-1171, October.
  6. Gary Hansen, 2010. "Indivisible Labor and the Business Cycle," Levine's Working Paper Archive 233, David K. Levine.
  7. Julio J. Rotemberg, 2003. "Stochastic Technical Progress, Smooth Trends, and Nearly Distinct Business Cycles," American Economic Review, American Economic Association, vol. 93(5), pages 1543-1559, December.
  8. 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.
  9. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2004. "The Response of Hours to a Technology Shock: Evidence Based on Direct Measures of Technology," Journal of the European Economic Association, MIT Press, vol. 2(2-3), pages 381-395, 04/05.
  10. Rodolfo E. Manuelli, 2000. "Technological Change, the Labor Market and the Stock Market," NBER Working Papers 8022, National Bureau of Economic Research, Inc.
  11. Hairault, Jean-Olivier & Langot, Francois & Portier, Franck, 1997. "Time to implement and aggregate fluctuations," Journal of Economic Dynamics and Control, Elsevier, vol. 22(1), pages 109-121, November.
  12. repec:cup:etheor:v:11:y:1995:i:5:p:1148-71 is not listed on IDEAS
  13. Gary D. Hansen & Edward C. Prescott, 1992. "Recursive methods for computing equilibria of business cycle models," Discussion Paper / Institute for Empirical Macroeconomics 36, Federal Reserve Bank of Minneapolis.
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