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

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  • Giovanni Peri

    (Department of Economics, University of California Davis)

Abstract

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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Bibliographic Info

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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Related research

Keywords: Innovation; Panel Cointegration; Error Correction Mechanism;

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References

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  1. Aghion, P. & Howitt, P., 1989. "A Model Of Growth Through Creative Destruction," Working papers 527, Massachusetts Institute of Technology (MIT), Department of Economics.
  2. Kao, Chihwa, 1999. "Spurious regression and residual-based tests for cointegration in panel data," Journal of Econometrics, Elsevier, Elsevier, vol. 90(1), pages 1-44, May.
  3. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth Through Creative Destruction," Scholarly Articles 12490578, Harvard University Department of Economics.
  4. Edmond, Chris, 2001. "Some Panel Cointegration Models of International R&D Spillovers," Journal of Macroeconomics, Elsevier, Elsevier, vol. 23(2), pages 241-260, April.
  5. Galí, Jordi, 1996. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," CEPR Discussion Papers, C.E.P.R. Discussion Papers 1499, C.E.P.R. Discussion Papers.
  6. Paul Romer, 1989. "Endogenous Technological Change," NBER Working Papers 3210, National Bureau of Economic Research, Inc.
  7. 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 Working Papers 4370, National Bureau of Economic Research, Inc.
  8. John Y. Campbell & Pierre Perron, 1991. "Pitfalls and Opportunities: What Macroeconomists Should Know About Unit Roots," NBER Technical Working Papers 0100, National Bureau of Economic Research, Inc.
  9. Wolfgang Keller, 2002. "Geographic Localization of International Technology Diffusion," American Economic Review, American Economic Association, vol. 92(1), pages 120-142, March.
  10. 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.
  11. Peter C. B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Econometrica, Econometric Society, Econometric Society, vol. 67(5), pages 1057-1112, September.
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