Growth Empirics in Panel Data Under Model Uncertainty and Weak Exogeneity
结合贝叶斯模型平均与动态面板模型，处理模型不确定性和反向因果问题，发现1960-2000年间跨国数据中九个候选变量均非增长的稳健决定因素，且条件收敛率接近零。
This paper considers panel growth regressions in the presence of model uncertainty and reverse causality concerns. For this purpose, my econometric framework combines Bayesian model averaging with a suitable likelihood function for dynamic panel models with weakly exogenous regressors and fixed effects. An application of this econometric methodology to a panel of countries over the 1960–2000 period highlights the difficulties in identifying the sources of economic growth by means of cross-country regressions. In particular, none of the nine candidate regressors considered can be labeled as a robust determinant of economic growth. Moreover, the estimated rate of conditional convergence is indistinguishable from zero. Copyright © 2014 John Wiley & Sons, Ltd.