Assessing the contribution of R&D to total factor productivity-a Bayesian approach to account for heterogeneity and heteroskedasticity

摘要

This paper proposes a hierarchical Bayes estimator for a panel data random coefficient model with heteroskedasticity to assess the contribution of R&D capital to total factor productivity. Based on Hall (1993) data for 323 US firms over 1976-1990, we find that there appear to have substantial unobserved heterogeneity and heteroskedasticity across firms and industries that support the use of our Bayes inference procedure. We find much higher returns to R&D capital and a more pronounced downswing for the 1981-1985 period, followed by a more pronounced upswing than those yielded by the conventional feasible generalized least squares estimators or other estimates. The estimated elasticities of R&D capital are 0.062 for 1976-1980, 0.036 for 1981-1985 and 0.081 for 1986-1990, while the estimated elasticities of ordinary capital are much more stable over these periods.