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

• Published in: American journal of agricultural economics Vol. 92; no. 4; pp. 985 - 998
• Main Authors: Balcombe, Kelvin, Rapsomanikis, George
• Format: Journal Article
• Language: English
• Published: Malden Oxford University Press 01.07.2010; Oxford University press; Blackwell Publishing Ltd
• Subjects: 1889-1990; Agrarwissenschaft; Agricultural development; Agricultural economics; Agricultural production; Agricultural productivity; agriculture; Agronomy; Algorithms; Bayesian; Bayesian analysis; Bayesian method; Bayesian networks; Bayesian theory; Breaking; C22; C51; Economic models; Economics; Empirical evidence; Gestation period; Impact analysis; Landwirtschaft; Mathematical models; Parametric models; probability; Productivity; Produktivität; R&D; Research & development; Research and development; Reversible; reversible jump; Standard deviation; Studies; United States; USA; Usefulness; United States > US; United States; agriculture; C22; reversible jump; productivity; O3; Bayesian; research and development; C51; Q0
• ISSN: 0002-9092; 1467-8276; 1467-8276
• 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 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.