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

  • Kelvin Balcombe


  • Alastair Bailey


  • Iain Fraser


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

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Article provided by Springer in its journal Journal of Productivity Analysis.

Volume (Year): 24 (2005)
Issue (Month): 1 (09)
Pages: 49-72

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Handle: RePEc:kap:jproda:v:24:y:2005:i:1:p:49-72
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  1. Huffman, Wallace E. & Evenson, Robert E., 2006. "Science for Agriculture: A Long Term Perspective," Staff General Research Papers 12362, Iowa State University, Department of Economics.
  2. Phillips, Peter C. B., 1995. "Bayesian model selection and prediction with empirical applications," Journal of Econometrics, Elsevier, vol. 69(1), pages 289-331, September.
  3. Carmen Fernandez & Eduardo Ley & Mark F J Steel, 1998. "Benchmark priors for Bayesian model averaging," ESE Discussion Papers 66, Edinburgh School of Economics, University of Edinburgh.
  4. 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.
  5. 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.
  6. 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 of Agricultural Economists, International Association of Agricultural Economists, vol. 19(1-2), September.
  7. 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.
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