Growth Determinants Revisited Using Limited‐Information Bayesian Model Averaging
提出一种新的有限信息贝叶斯模型平均方法，用于处理短面板数据中的模型不确定性、动态性和内生性问题，并重新识别了与经济增长稳健相关的因素，包括新古典增长变量、宏观经济政策、地理和种族异质性。
Summary We revisit the growth empirics debate using a novel limited‐information Bayesian model averaging framework in short T panels that addresses model uncertainty, dynamics, and endogeneity. We construct an estimator without restrictive distributional assumptions, illustrate its performance using simulations, and apply it to the investigation of growth determinants. Once model uncertainty, dynamics, and endogeneity are accounted for, we identify several factors that are robustly correlated with growth. We find the strongest support for the neoclassical growth variables including initial income and proxies for physical and human capital accumulation, as well as evidence in favor of both fundamental and proximate factors including macroeconomic policy, geography, and ethnic heterogeneity. In addition, we demonstrate that applying methodologies that do not account for either dynamics or endogeneity yields different sets of robust determinants. Copyright © 2015 John Wiley & Sons, Ltd.