An Analysis of the Impact of Research and Development on Productivity Using Bayesian Model Averaging with a Reversible Jump Algorithm
引入贝叶斯模型平均方法估计滞后结构，并用可逆跳跃算法分析1889-1990年美国研发对农业生产率的影响，发现研发正向驱动生产率。
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.