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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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File URL: http://wp.econ.ucdavis.edu/03-7.pdf
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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, 2002. "Geographic Localization of International Technology Diffusion," American Economic Review, American Economic Association, vol. 92(1), pages 120-142, March.
  2. Philippe Aghion & Peter Howitt, 1990. "A Model of Growth Through Creative Destruction," NBER Working Papers 3223, National Bureau of Economic Research, Inc.
  3. Paul M Romer, 1999. "Endogenous Technological Change," Levine's Working Paper Archive 2135, David K. Levine.
  4. Jordi Gali, 1996. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations," NBER Working Papers 5721, National Bureau of Economic Research, Inc.
  5. Campbell, J.Y. & Perron, P., 1991. "Pitfalls and Opportunities: What Macroeconomics should know about unit roots," Papers 360, Princeton, Department of Economics - Econometric Research Program.
  6. 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.
  7. Nelson C. Mark & Masao Ogaki & Donggyu Sul, 2005. "Dynamic Seemingly Unrelated Cointegrating Regressions," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 797-820.
  8. 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.
  9. Edmond, Chris, 2001. "Some Panel Cointegration Models of International R&D Spillovers," Journal of Macroeconomics, Elsevier, vol. 23(2), pages 241-260, April.
  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.
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