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The delayed response to a technology shock: a flexible price explanation

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  • Robert J. Vigfusson

Abstract

I present empirical evidence of how the U.S. economy, including per-capita hours worked, responds to a technology shock. In particular, I present results based on permanent changes to a constructed direct measure of technological change for U.S. manufacturing industries. Based on empirical evidence, some claim that hours worked declines and never recovers in response to a positive technology shock. This paper's empirical evidence suggests that emphasizing the drop in hours worked is misdirected. Because the sharp drop in hours is not present here, the emphasis rather should be on the small (perhaps negative) initial response followed by a subsequent large positive response. Investment, consumption, and output have similar dynamic responses. In response to a positive technology shock, a standard flexible price model would have an immediate increase in hours worked. Therefore, such a model is inconsistent with the empirical dynamic responses. I show, however, that a flexible price model with habit persistence in consumption and certain kinds of capital adjustment costs can better match the empirical responses. Some recent papers have critiqued the use of long run VARs to identify the dynamic responses to a technology shock. In particular they report that, when long run VARs are applied to data simulated from particular economic models, the point estimates of the impulse responses may be imprecisely estimated. However, based on additional simulation evidence, I find that, although the impact response may be imprecisely estimated, a finding of a delayed response is much more likely when the true model response also has a delayed response.

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Bibliographic Info

Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series International Finance Discussion Papers with number 810.

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Date of creation: 2004
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Handle: RePEc:fip:fedgif:810

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Related research

Keywords: Technology - Economic aspects ; Hours of labor ; Mathematical models;

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References

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  1. Lawrence J. Christiano, 1998. "Solving dynamic equilibrium models by a method of undetermined coefficients," Working Paper 9804, Federal Reserve Bank of Cleveland.
  2. Burnside, Craig, 1996. "Production function regressions, returns to scale, and externalities," Journal of Monetary Economics, Elsevier, vol. 37(2-3), pages 177-201, April.
  3. Jermann, Urban J., 1998. "Asset pricing in production economies," Journal of Monetary Economics, Elsevier, vol. 41(2), pages 257-275, April.
  4. Robert G. King & Charles I. Plosser & James H. Stock & Mark W. Watson, 1987. "Stochastic Trends and Economic Fluctuations," NBER Working Papers 2229, National Bureau of Economic Research, Inc.
  5. Craig Burnside & Martin Eichenbaum & Sergio Rebelo, 1995. "Sectoral Solow residuals," Working Paper Series, Macroeconomic Issues 95-15, Federal Reserve Bank of Chicago.
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Cited by:
  1. Ippei Fujiwara & Yasuo Hirose & Mototsugu Shintani, 2008. "Can News Be a Major Source of Aggregate Fluctuations? A Bayesian DSGE Approach," Levine's Working Paper Archive 122247000000002352, David K. Levine.
  2. Fabio Canova & David López-Salido & Claudio Michelacci, 2006. "On the robust effects of technology shocks on hours worked and output," Economics Working Papers 1013, Department of Economics and Business, Universitat Pompeu Fabra, revised Feb 2008.
  3. Holly, S. & Petrella, I., 2010. "Factor Demand Linkages, Technology Shocks and the Business Cycle," Cambridge Working Papers in Economics 1001, Faculty of Economics, University of Cambridge.
  4. Luca Dedola & Stefano Neri, 2006. "What does a technology shock do? A VAR analysis with model-based sign restrictions," Temi di discussione (Economic working papers) 607, Bank of Italy, Economic Research and International Relations Area.
  5. Patrick Fève & Alain Guay, 2010. "Identification of Technology Shocks in Structural Vars," Economic Journal, Royal Economic Society, vol. 120(549), pages 1284-1318, December.
  6. Fabio Canova & David Lopez-Salido & Claudio Michelacci, 2010. "The effects of technology shocks on hours and output: a robustness analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 755-773.
  7. Fujiwara, Ippei & Teranishi, Yuki, 2008. "A dynamic new Keynesian life-cycle model: Societal aging, demographics, and monetary policy," Journal of Economic Dynamics and Control, Elsevier, vol. 32(8), pages 2398-2427, August.
  8. Holly, S. & Petrella, I., 2008. "Factor demand linkages and the business cycle: Interpreting aggregate fluctuations as sectoral fluctuations," Cambridge Working Papers in Economics 0827, Faculty of Economics, University of Cambridge.
  9. Pengfei Wang & Yi Wen, 2011. "Understanding the Effects of Technology Shocks," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(4), pages 705-724, October.

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