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Breaking the Waves: A Poisson Regression Approach to Schumpeterian Clustering of Basic Innovations

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  • Silverberg, Gerald
  • Verspagen, Bart

    (MERIT)

Abstract

The Schumpeterian theory of long waves has given rise to an intense debate on the existenceof clusters of basic innovations. Silverberg and Lehnert have criticized the empirical part ofthis literature on several methodological accounts. In this paper, we propose the methodologyof Poisson regression as a logical way to incorporate this criticism. We construct a new timeseries for basic innovations (based on previously used time series), and use this to test thehypothesis that basic innovations cluster in time. We define the concept of clustering invarious precise ways before undertaking the statistical tests. The evidence we find onlysupports the �weakest� of our clustering hypotheses, i.e., that the data display overdispersion.We thus conclude that the authors who have argued that a long wave in economic life isdriven by clusters of basic innovations have stretched the statistical evidence too far.

Suggested Citation

  • Silverberg, Gerald & Verspagen, Bart, 2000. "Breaking the Waves: A Poisson Regression Approach to Schumpeterian Clustering of Basic Innovations," Research Memorandum 026, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
  • Handle: RePEc:unm:umamer:2000026
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    References listed on IDEAS

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    1. Silverberg, Gerald & Lehnert, Doris, 1993. "Long waves and 'evolutionary chaos' in a simple Schumpeterian model of embodied technical change," Structural Change and Economic Dynamics, Elsevier, vol. 4(1), pages 9-37, June.
    2. Crepon, Bruno & Duguet, Emmanuel, 1997. "Research and development, competition and innovation pseudo-maximum likelihood and simulated maximum likelihood methods applied to count data models with heterogeneity," Journal of Econometrics, Elsevier, vol. 79(2), pages 355-378, August.
    3. Robert J. Gordon, 2000. "Does the "New Economy" Measure Up to the Great Inventions of the Past?," Journal of Economic Perspectives, American Economic Association, vol. 14(4), pages 49-74, Fall.
    4. Alfred Kleinknecht, 1987. "Innovation Patterns in Crisis and Prosperity," Palgrave Macmillan Books, Palgrave Macmillan, number 978-1-349-18559-7, December.
    5. Cincera, Michele, 1997. "Patents, R&D, and Technological Spillovers at the Firm Level: Some Evidence from Econometric Count Models for Panel Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 265-280, May-June.
    6. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
    7. Solomou, Solomos, 1986. "Innovation Clusters and Kondratieff Long Waves in Economic Growth," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 10(2), pages 101-112, June.
    8. Crepon, Bruno & Duguet, Emmanuel, 1997. "Estimating the Innovation Function from Patent Numbers: GMM on Count Panel Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 243-263, May-June.
    9. Chris Freeman & Luc Soete, 1997. "The Economics of Industrial Innovation, 3rd Edition," MIT Press Books, The MIT Press, edition 3, volume 1, number 0262061953, April.
    10. Kleinknecht, Alfred, 1990. "Are There Schumpeterian Waves of Innovations?," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 14(1), pages 81-92, March.
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