Robust Estimation of Large Panels with Factor Structures
研究当回归变量和误差项可能包含共同潜在因子结构时，如何用加权最小二乘估计线性面板回归模型中的异质系数，该方法无需知道因子数量或估计因子结构，且在某些情况下具有GLS效率。
This article studies estimation of linear panel regression models with heterogeneous coefficients using a class of weighted least squares estimators, when both the regressors and the error possibly contain a common latent factor structure. Our theory is robust to the specification of such a factor structure because it does not require any information on the number of factors or estimation of the factor structure itself. Moreover, our theory is efficient, in certain circumstances, because it nests the GLS principle. We first show how our unfeasible weighted-estimator provides a bias-adjusted estimator with the conventional limiting distribution, for situations in which the OLS is affected by a first-order bias. The technical challenge resolved in the article consists of showing how these properties are preserved for the feasible weighted estimator in a double-asymptotics setting. Our theory is illustrated by extensive Monte Carlo experiments and an empirical application that investigates the link between capital accumulation and economic growth in an international setting. Supplementary materials for this article are available online.