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An Analysis of the Impact of Research and Development on Productivity Using Bayesian Model Averaging with a Reversible Jump Algorithm

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  • Kelvin Balcombe
  • George Rapsomanikis

Abstract

A Bayesian model averaging approach to the estimation of lag structures is introduced and applied to assess the impact of (R&D) on agricultural productivity in the United States from 1889 to 1990. Lag and structural break coefficients are estimated using a reversible jump algorithm that traverses the model space. In addition to producing estimates and standard deviations for the coefficients, the probability that a given lag (or break) enters the model is estimated. The approach is extended to select models populated with gamma distributed lags of different frequencies. Results are consistent with the hypothesis that R&D positively drives productivity. Gamma lags are found to retain their usefulness in imposing a plausible structure on lag coefficients, and their role is enhanced through the use of model averaging. Copyright 2010, Oxford University Press.

Suggested Citation

  • Kelvin Balcombe & George Rapsomanikis, 2010. "An Analysis of the Impact of Research and Development on Productivity Using Bayesian Model Averaging with a Reversible Jump Algorithm," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(4), pages 985-998.
    • Handle: RePEc:oup:ajagec:v:92:y:2010:i:4:p:985-998
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    File URL: http://hdl.handle.net/10.1093/ajae/aaq050
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    Cited by:

    1. Tiffin, Richard & Balcombe, Kelvin, 2011. "The determinants of technology adoption by UK farmers using Bayesian model averaging: the cases of organic production and computer usage," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 55(4), pages 1-20.
    2. Mirela Stoian & Raluca Andreea Ion & Vlad Constantin Turcea & Ionut Catalin Nica & Catalin Gheorghe Zemeleaga, 2022. "The Influence of Governmental Agricultural R&D Expenditure on Farmers’ Income—Disparities between EU Member States," Sustainability, MDPI, vol. 14(17), pages 1-14, August.
    3. Claudia Schmidt & Steven C. Deller & Stephan J. Goetz, 2024. "Women farmers and community well‐being under modeling uncertainty," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 46(1), pages 275-299, March.
    4. Gong, Binlei, 2020. "Measuring and Achieving World Agricultural Convergence," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304347, Agricultural and Applied Economics Association.
    5. Lingran Yuan & Shurui Zhang & Shuo Wang & Zesen Qian & Binlei Gong, 2021. "World agricultural convergence," Journal of Productivity Analysis, Springer, vol. 55(2), pages 135-153, April.
    6. Ebersberger, Bernd & Galia, Fabrice & Laursen, Keld & Salter, Ammon, 2021. "Inbound Open Innovation and Innovation Performance: A Robustness Study," Research Policy, Elsevier, vol. 50(7).

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