Multifactor error structures utilize factor analysis to deal with complex cross-sectional dependence in Time-Series Cross-Sectional data caused by cross-level interactions. The multifactor error structure specification is a generalization of the fixed-effects model. This article extends the existing multifactor error models from panel econometrics to multilevel modeling, from linear setups to generalized linear models with the probit and logistic links, and from assuming serial independence to modeling the error dynamics with an autoregressive process. I develop Markov Chain Monte Carlo algorithms mixed with a rejection sampling scheme to estimate the multilevel multifactor error structure model with a pth-order autoregressive process in linear, probit, and logistic specifications. I conduct several Monte Carlo studies to compare the performance of alternative specifications and approaches with varying degrees of data complication and different sample sizes. The Monte Carlo studies provide guidance on when and how to apply the proposed model. An empirical application sovereign default demonstrates how the proposed approach can accommodate a complex pattern of cross-sectional dependence and helps answer research questions related to units' sensitivity or vulnerability to systemic shocks.
<正>哈佛大学教授尤查·本克勒(Yochai Benkler)最早开始关注由个人以及或松散或紧密的合作者进行的非市场化、非专有化的生产,认定它们在信息、知识和文化交换中所起的作用日益加大。他为此撰写了煌煌大作《网络的财富》(The Wealth of Networks,2006年出版),该书虽然火花四射,但却是以学术方式撰写的大部头著作,读来令人头疼。或许是为了证明自己也会讲故事,2011年本克勒的下一本著作《企鹅与怪兽》(The Penguin and the Leviathan)一反常态,充满了各种轶事,连一个脚注、一本参考文献都没有。它堪称一本有关人