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Measuring the impact of R&D on Productivity from a Econometric Time Series Perspective

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  • Kelvin Balcombe
  • Alastair Bailey
  • Iain Fraser

Abstract

In this paper we argue that the standard sequential reduction approach to modelling dynamic relationships may be sub-optimal when long lag lengths are required and especially when the intermediate lags may be less important. A flexible model search approach is adopted using the insights of Bayesian Model probabilities, and new information criteria based on forecasting performance. This approach is facilitated by exploiting Genetic Algorithms. Using data on U.K. and U.S. agriculture the bivariate time series relationship between R&D expenditure and productivity is analysed. Long lags are found in the relationship between R&D expenditures and productivity in the U.K. and in the U.S. which remain undiscovered when using the orthodox approach. This finding is of particular importance in the debate on the optimal level of public R&D funding. Copyright Springer Science+Business Media, Inc. 2005

Suggested Citation

  • Kelvin Balcombe & Alastair Bailey & Iain Fraser, 2005. "Measuring the impact of R&D on Productivity from a Econometric Time Series Perspective," Journal of Productivity Analysis, Springer, vol. 24(1), pages 49-72, September.
    • Handle: RePEc:kap:jproda:v:24:y:2005:i:1:p:49-72
    DOI: 10.1007/s11123-005-3040-x
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    References listed on IDEAS

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    2. Stéphane Lemarié & Valérie Orozco & Jean-Pierre Butault & Antonio Musolesi & Michel Simioni & Bertrand Schmitt, 2020. "Assessing the long-term impact of agricultural research on productivity: evidence from France," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(4), pages 1559-1586.
    3. Sajid Anwar & Sizhong Sun, 2014. "Entry of foreign firms and the R&D behaviour: a panel data study of domestic and foreign firms in China's manufacturing sector," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 23(8), pages 739-757, November.
    4. Elena Makrevska Disoska & Katerina Toshevska-Trpchevska & Dragan Tevdovski & Petar Jolakoski & Viktor Stojkoski, 2024. "A Pooled Overview of the European National Innovation Systems Through the Lenses of the Community Innovation Survey," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 3660-3684, March.
    5. Donghyuk Choi & Joseph Kang & Chiyong Kim, 2014. "Effect of R&D on firms? growth: discrepancy between sales growth and employment expansion," Proceedings of Economics and Finance Conferences 0401582, International Institute of Social and Economic Sciences.
    6. Kelvin Balcombe, 2005. "Model Selection Using Information Criteria and Genetic Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 25(3), pages 207-228, June.
    7. Andersen, Matthew A., 2019. "Knowledge productivity and the returns to agricultural research: a review," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 63(2), April.

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