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The use of long-run restrictions for the identification of technology shocks

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  • Neville Francis
  • Michael T. Owyang
  • Athena T. Theodorou

Abstract

We survey the recent empirical literature using long run restrictions to identify technology shocks. We provide an illustrative walkthrough of the long-run restricted vector autoregression (VAR) methodology in a bivariate framework. Additionally, we offer an alternative identification of technology shocks that can be imposed by restrictions on the long-run impulse responses. Our results from this methodology compare favorably to the empirical literature that uses structural VARs to identify technology.

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Bibliographic Info

Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2003-010.

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Date of creation: 2003
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Publication status: Published in Federal Reserve Bank of St. Louis Review, November/December 2003, 85(6), pp. 53-66
Handle: RePEc:fip:fedlwp:2003-010

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Keywords: Business cycles ; Technology;

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  1. Uhlig, H.F.H.V.S., 1999. "What are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure," Discussion Paper 1999-28, Tilburg University, Center for Economic Research.
  2. Andrew Mountford & Harald Uhlig, 2009. "What are the effects of fiscal policy shocks?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(6), pages 960-992.
  3. Canova, Fabio & Nicolo, Gianni De, 2002. "Monetary disturbances matter for business fluctuations in the G-7," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1131-1159, September.
  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. Susanto Basu & John Fernald & Miles Kimball, 2002. "Are Technology Improvements Contractionary?," Harvard Institute of Economic Research Working Papers 1986, Harvard - Institute of Economic Research.
  6. 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.
  7. John Shea, 1998. "What Do Technology Shocks Do?," NBER Working Papers 6632, National Bureau of Economic Research, Inc.
  8. Robert E. Hall, 1986. "The Relation Between Price and Marginal Cost in U.S. Industry," NBER Working Papers 1785, National Bureau of Economic Research, Inc.
  9. Matthew D. Shapiro & Mark W. Watson, 1988. "Sources of Business Cycle Fluctuations," Cowles Foundation Discussion Papers 870, Cowles Foundation for Research in Economics, Yale University.
  10. 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.).
  11. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-73, September.
  12. Evans, Charles L., 1992. "Productivity shocks and real business cycles," Journal of Monetary Economics, Elsevier, vol. 29(2), pages 191-208, April.
  13. 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.
  14. Michael T. Owyang, 2002. "Modeling Volcker as a non-absorbing state: agnostic identification of a Markov-switching VAR," Working Papers 2002-018, Federal Reserve Bank of St. Louis.
  15. Faust, Jon, 1998. "The robustness of identified VAR conclusions about money," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 49(1), pages 207-244, December.
  16. Jon Faust, 1998. "The robustness of identified VAR conclusions about money," International Finance Discussion Papers 610, Board of Governors of the Federal Reserve System (U.S.).
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