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

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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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  • Robert J. Vigfusson, 2004. "The delayed response to a technology shock: a flexible price explanation," International Finance Discussion Papers 810, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgif:810
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    1. Jermann, Urban J., 1998. "Asset pricing in production economies," Journal of Monetary Economics, Elsevier, vol. 41(2), pages 257-275, April.
    2. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, vol. 81(4), pages 819-840, September.
    3. Burnside, A. Craig & Eichenbaum, Martin S. & Rebelo, Sergio T., 1996. "Sectoral Solow residuals," European Economic Review, Elsevier, vol. 40(3-5), pages 861-869, April.
    4. Christiano, Lawrence J, 2002. "Solving Dynamic Equilibrium Models by a Method of Undetermined Coefficients," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 21-55, October.
    5. Burnside, Craig, 1996. "Production function regressions, returns to scale, and externalities," Journal of Monetary Economics, Elsevier, vol. 37(2-3), pages 177-201, April.
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    Cited by:

    1. Patrick Fève & Alain Guay, 2010. "Identification of Technology Shocks in Structural Vars," Economic Journal, Royal Economic Society, vol. 120(549), pages 1284-1318, December.
    2. 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.
    3. Sean Holly & Ivan Petrella, 2008. "Factor demand linkages and the business cycle: interpreting aggregate fluctuations as sectoral fluctuations," CDMA Conference Paper Series 0809, Centre for Dynamic Macroeconomic Analysis.
    4. Sean Holly & Ivan Petrella, 2012. "Factor Demand Linkages, Technology Shocks, and the Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 948-963, November.
    5. Dedola, Luca & Neri, Stefano, 2007. "What does a technology shock do? A VAR analysis with model-based sign restrictions," Journal of Monetary Economics, Elsevier, vol. 54(2), pages 512-549, March.
    6. Ippei Fujiwara & Yasuo Hirose & Mototsugu Shintani, 2011. "Can News Be a Major Source of Aggregate Fluctuations? A Bayesian DSGE Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(1), pages 1-29, February.
    7. 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.
    8. Dedola, Luca & Neri, Stefano, 2007. "What does a technology shock do? A VAR analysis with model-based sign restrictions," Journal of Monetary Economics, Elsevier, vol. 54(2), pages 512-549, March.
    9. Christopher J. Gust & Robert J. Vigfusson, 2009. "The power of long-run structural VARs," International Finance Discussion Papers 978, Board of Governors of the Federal Reserve System (U.S.).
    10. 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.
    11. 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.
    12. Yi Wen & Pengfei Wang, 2008. "A Defense of RBC:Understanding the Puzzling Effects of Technology Shocks," 2008 Meeting Papers 7, Society for Economic Dynamics.

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    Keywords

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