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What Happens After a Technology Shock?

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  • Lawrence J. Christiano
  • Martin Eichenbaum
  • Robert Vigfusson

Abstract

We provide empirical evidence that a positive shock to technology drives per capita hours worked, consumption, investment, average productivity and output up. This evidence contrasts sharply with the results reported in a large and growing literature that argues, on the basis of aggregate data, that per capita hours worked fall after a positive technology shock. We argue that the difference in results primarily reflects specification error in the way that the literature models the low-frequency component of hours worked.

Suggested Citation

  • Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What Happens After a Technology Shock?," NBER Working Papers 9819, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:9819
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    References listed on IDEAS

    as
    1. Miles S. Kimball & John G. Fernald & Susanto Basu, 2006. "Are Technology Improvements Contractionary?," American Economic Review, American Economic Association, vol. 96(5), pages 1418-1448, December.
    2. Caner, M. & Kilian, L., 2001. "Size distortions of tests of the null hypothesis of stationarity: evidence and implications for the PPP debate," Journal of International Money and Finance, Elsevier, vol. 20(5), pages 639-657, October.
    3. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148, Elsevier.
    4. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    5. Boldrin, Michele & Christiano, Lawrence J. & Fisher, Jonas D. M., 1995. "Asset pricing lessons for modeling business cycles," UC3M Working papers. Economics 3915, Universidad Carlos III de Madrid. Departamento de Economía.
    6. Elliott, Graham & Jansson, Michael, 2003. "Testing for unit roots with stationary covariates," Journal of Econometrics, Elsevier, vol. 115(1), pages 75-89, July.
    7. Christiano, Lawrence J & Eichenbaum, Martin, 1992. "Current Real-Business-Cycle Theories and Aggregate Labor-Market Fluctuations," American Economic Review, American Economic Association, vol. 82(3), pages 430-450, June.
    8. Christiano, Lawrence J. & Eichenbaum, Martin, 1990. "Unit roots in real GNP: Do we know, and do we care?," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 32(1), pages 7-61, January.
    9. DeJong, David N, et al, 1992. "Integration versus Trend Stationarity in Time Series," Econometrica, Econometric Society, vol. 60(2), pages 423-433, March.
    10. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    11. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "The Band Pass Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, May.
    12. Gali, Jordi & Lopez-Salido, J. David & Valles, Javier, 2003. "Technology shocks and monetary policy: assessing the Fed's performance," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 723-743, May.
    13. Hansen, Bruce E., 1995. "Rethinking the Univariate Approach to Unit Root Testing: Using Covariates to Increase Power," Econometric Theory, Cambridge University Press, vol. 11(5), pages 1148-1171, October.
    14. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
    15. 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.
    16. Jordi Galí & Mark Gertler & J. David López-Salido, 2007. "Markups, Gaps, and the Welfare Costs of Business Fluctuations," The Review of Economics and Statistics, MIT Press, vol. 89(1), pages 44-59, November.
    17. Matthew D. Shapiro & Mark W. Watson, 1988. "Sources of Business Cycle Fluctuations," NBER Chapters, in: NBER Macroeconomics Annual 1988, Volume 3, pages 111-156, National Bureau of Economic Research, Inc.
    18. repec:cup:etheor:v:11:y:1995:i:5:p:1148-71 is not listed on IDEAS
    19. Lawrence J. Christiano & Lars Ljungqvist, 1987. "Money does Granger-cause output in the bivariate output-money relation," Staff Report 108, Federal Reserve Bank of Minneapolis.
    20. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    21. Christiano, Lawrence J. & Ljungqvist, Lars, 1988. "Money does Granger-cause output in the bivariate money-output relation," Journal of Monetary Economics, Elsevier, vol. 22(2), pages 217-235, September.
    22. Lawrence J. Christiano & Richard M. Todd, 1996. "Time to plan and aggregate fluctuations," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 20(Win), pages 14-27.
    23. James H. Stock & Motohiro Yogo, 2002. "Testing for Weak Instruments in Linear IV Regression," NBER Technical Working Papers 0284, National Bureau of Economic Research, Inc.
    24. John Shea, 1999. "What Do Technology Shocks Do?," NBER Chapters, in: NBER Macroeconomics Annual 1998, volume 13, pages 275-322, National Bureau of Economic Research, Inc.
    25. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    26. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
    27. Jonas D. M. Fisher, 2002. "Technology shocks matter," Working Paper Series WP-02-14, Federal Reserve Bank of Chicago.
    28. 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.
    29. Martin Eichenbaum & Kenneth I. Singleton, 1986. "Do Equilibrium Real Business Cycle Theories Explain Postwar US Business Cycles?," NBER Chapters, in: NBER Macroeconomics Annual 1986, Volume 1, pages 91-146, National Bureau of Economic Research, Inc.
    30. Jinyong Hahn & Jerry Hausman & Guido Kuersteiner, 2005. "Bias Corrected Instrumental Variables Estimation for Dynamic Panel Models with Fixed E¤ects," Boston University - Department of Economics - Working Papers Series WP2005-024, Boston University - Department of Economics.
    31. Leybourne, S J & McCabe, B P M, 1994. "A Consistent Test for a Unit Root," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 157-166, April.
    Full references (including those not matched with items on IDEAS)

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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