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

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  • Kelvin Balcombe

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  • Alastair Bailey

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  • 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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    1. Shenggen Fan, 2000. "Research Investment and the Economic Returns To Chinese Agricultural Research," Journal of Productivity Analysis, Springer, vol. 14(2), pages 163-182, September.
    2. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    3. Phillips, Peter C. B., 1995. "Bayesian model selection and prediction with empirical applications," Journal of Econometrics, Elsevier, vol. 69(1), pages 289-331, September.
    4. Huffman, Wallace E. & Evenson, Robert E., 1993. "Science for Agriculture: A Long Term Perspective," Staff General Research Papers Archive 10997, Iowa State University, Department of Economics.
    5. Thirtle, C. & Bottomley, P. & Palladino, P. & Schimmelpfennig, D. & Townsend, R., 1998. "The rise and fall of public sector plant breeding in the United Kingdom: a causal chain model of basic and applied research and diffusion," Agricultural Economics, Blackwell, vol. 19(1-2), pages 127-143, September.
    6. Chao, John C. & Phillips, Peter C. B., 1999. "Model selection in partially nonstationary vector autoregressive processes with reduced rank structure," Journal of Econometrics, Elsevier, vol. 91(2), pages 227-271, August.
    7. G. Duggal, Vijaya & Saltzman, Cynthia & Klein, Lawrence R., 1999. "Infrastructure and productivity: a nonlinear approach," Journal of Econometrics, Elsevier, vol. 92(1), pages 47-74, September.
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    Cited by:

    1. 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.
    2. 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.

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