SPATIAL FILTERING, MODEL UNCERTAINTY AND THE SPEED OF INCOME CONVERGENCE IN EUROPE
提出一种贝叶斯模型平均方法，在存在空间自相关时处理模型不确定性，应用于欧洲255个地区的人均收入增长数据，发现人力资本投资和收入收敛的过渡动态是区域增长最稳健的决定因素。
SUMMARY In this paper we put forward a Bayesian model averaging method aimed at performing inference under model uncertainty in the presence of potential spatial autocorrelation. The method uses spatial filtering in order to account for uncertainty in spatial linkages. Our procedure is applied to a dataset of income per capita growth and 50 potential determinants for 255 NUTS‐2 European regions. We show that ignoring uncertainty in the type of spatial weight matrix can have an important effect on the estimates of the parameters attached to the model covariates. After integrating out the uncertainty implied by the choice of regressors and spatial links, human capital investments and transitional dynamics related to income convergence appear as the most robust determinants of growth at the regional level in Europe. Our results imply that a quantitatively important part of the income convergence process in Europe is influenced by spatially correlated growth spillovers. Copyright © 2012 John Wiley & Sons, Ltd.