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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.

Suggested Citation

  • Giovanni Peri, 2003. "The Dynamics of R&D and Innovation in the Long Run and in the Short Run," Working Papers 13, University of California, Davis, Department of Economics.
  • Handle: RePEc:cda:wpaper:13
    as

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    File URL: http://repec.dss.ucdavis.edu/files/6MDQEcgs5SE3aqsKw2TrczHh/03-7.pdf
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    References listed on IDEAS

    as
    1. Keller, Wolfgang, 1998. "Are international R&D spillovers trade-related?: Analyzing spillovers among randomly matched trade partners," European Economic Review, Elsevier, vol. 42(8), pages 1469-1481, September.
    2. 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.
    3. Edmond, Chris, 2001. "Some Panel Cointegration Models of International R&D Spillovers," Journal of Macroeconomics, Elsevier, vol. 23(2), pages 241-260, April.
    4. Wolfgang Keller, 2002. "Geographic Localization of International Technology Diffusion," American Economic Review, American Economic Association, vol. 92(1), pages 120-142, March.
    5. Christiano, Lawrence J & Eichenbaum, Martin, 1992. "Current Real-Business-Cycle Theories and Aggregate Labor-Market Fluctuations," American Economic Review, American Economic Association, vol. 82(3), pages 430-450, June.
    6. 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.
    7. 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.
    8. Pedroni, Peter, 1999. " Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(0), pages 653-670, Special I.
    9. Peri, Giovanni, 2003. "Knowledge Flows, R&D Spillovers and Innovation," ZEW Discussion Papers 03-40, ZEW - Leibniz Centre for European Economic Research.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Innovation; Panel Cointegration; Error Correction Mechanism;

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • F43 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Economic Growth of Open Economies
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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