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

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  • PATRICK F�VE
  • ALAIN GUAY

Abstract

The response of hours to a technology shock is a controversial issue in macroeconomics. Part of the difficulty lies in that the estimated response is sensitive to the specification of hours in structural vector autoregressions (SVARs). This paper uses a simple two-step approach to consistently estimate the response of hours. The first step considers a SVAR model with a relevant stationary variable, 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. Simulation experiments from an estimated dynamic stochastic general equilibrium (DSGE) model show that this approach outperforms standard SVARs. When applied to U.S. data, the two-step approach predicts a short-run decrease followed by a hump-shaped positive response. This result is robust to other specifications and data. Copyright (c) 2009 The Ohio State University No claim to original US government works.

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Article provided by Blackwell Publishing in its journal Journal of Money, Credit and Banking.

Volume (Year): 41 (2009)
Issue (Month): 5 (08)
Pages: 987-1013

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Handle: RePEc:mcb:jmoncb:v:41:y:2009:i:5:p:987-1013

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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0022-2879

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  1. Kenneth D. West & Whitney K. Newey, 1995. "Automatic Lag Selection in Covariance Matrix Estimation," NBER Technical Working Papers 0144, National Bureau of Economic Research, Inc.
  2. 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.
  3. 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.
  4. Jordi Galí & Pau Rabanal, 2004. "Technology Shocks and Aggregate Fluctuations," IMF Working Papers 04/234, International Monetary Fund.
  5. 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.
  6. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What happens after a technology shock?," International Finance Discussion Papers 768, Board of Governors of the Federal Reserve System (U.S.).
  7. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2006. "Assessing structural VARs," International Finance Discussion Papers 866, Board of Governors of the Federal Reserve System (U.S.).
    • 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.
  8. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
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Cited by:
  1. Jidoud, Ahmat, 2012. "The Sources of Macroeconomic Fluctuations in Subsaharan African Economies: An application to Côte d'Ivoire," TSE Working Papers 12-346, Toulouse School of Economics (TSE).

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