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The Dynamics of R&D and Innovation in the Long Run and in the Short Run

  • Giovanni Peri

    (Department of Economics, University of California Davis)

In this paper we estimate the dynamic relationship between resources used in R&D by some OECD countries and their innovation output as measured by patent applications. We first estimate a long-run cointegration relation using recently developed tests and panel estimation techniques. We find that the stock of knowledge of a country, its R&D resources and the stock of international knowledge move together in the long run. Then, imposing this long-run relation across variables we analyze the impulse response of new ideas to a shock to R&D or to a shock to innovation by estimating an error correction mechanism. We find that internationally generated ideas have a very significant impact in helping innovation in a country. As a consequence, a positive shock to innovation in a large country as the US has, both in the short and in the long run, a significant positive effect on the innovation of all other countries.

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Paper provided by University of California, Davis, Department of Economics in its series Working Papers with number 37.

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Length: 33
Date of creation: 31 Jul 2003
Date of revision:
Handle: RePEc:cda:wpaper:03-7
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  1. Wolfgang Keller, 2000. "Geographic Localization of International Technology Diffusion," NBER Working Papers 7509, National Bureau of Economic Research, Inc.
  2. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth Through Creative Destruction," Scholarly Articles 12490578, Harvard University Department of Economics.
  3. Gali, J., 1996. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," Working Papers 96-28, C.V. Starr Center for Applied Economics, New York University.
  4. Paul Romer, 1989. "Endogenous Technological Change," NBER Working Papers 3210, National Bureau of Economic Research, Inc.
  5. Ricardo J. Caballero & Adam B. Jaffe, 1993. "How High are the Giants' Shoulders: An Empirical Assessment of Knowledge Spillovers and Creative Destruction in a Model of Economic Growth," NBER Chapters, in: NBER Macroeconomics Annual 1993, Volume 8, pages 15-86 National Bureau of Economic Research, Inc.
  6. Aghion, P. & Howitt, P., 1990. "A Model Of Growth Through Creative Destruction," DELTA Working Papers 90-12, DELTA (Ecole normale supérieure).
  7. Mark, Nelson & Ogaki, Masao & Sul, Donggyu, 2003. "Dynamic Seemingly Unrelated Cointegrating Regression," Working Papers 144, Department of Economics, The University of Auckland.
  8. John Y. Campbell & Pierre Perron, 1991. "Pitfalls and Opportunities: What Macroeconomists Should Know About Unit Roots," NBER Chapters, in: NBER Macroeconomics Annual 1991, Volume 6, pages 141-220 National Bureau of Economic Research, Inc.
  9. Peter C.B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Cowles Foundation Discussion Papers 1222, Cowles Foundation for Research in Economics, Yale University.
  10. Kao, Chihwa, 1999. "Spurious regression and residual-based tests for cointegration in panel data," Journal of Econometrics, Elsevier, vol. 90(1), pages 1-44, May.
  11. Edmond, Chris, 2001. "Some Panel Cointegration Models of International R&D Spillovers," Journal of Macroeconomics, Elsevier, vol. 23(2), pages 241-260, April.
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