An analysis of the impact of R&D on productivity using Bayesian model averaging with a reversible jump algorithm.
Balcombe, K. G. and Rapsomanikis, G. (2010) An analysis of the impact of R&D on productivity using Bayesian model averaging with a reversible jump algorithm. American Journal of Agricultural Economics, 92 (4). pp. 985-998. ISSN 0002-9092
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To link to this item DOI: 10.1093/ajae/aaq050
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 US 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 coe¢ cients, 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 di¤erent 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 coe¢ cients, and their role is enhanced through the use of model averaging.