INTERACTIVE EFFECTS PANEL DATA MODELS WITH GENERAL FACTORS AND REGRESSORS
提出一种基于迭代主成分分析的估计方法，用于处理含不可观测公共因子和一般回归变量的面板数据模型，该方法在特定条件下可避免渐近 incidental 参数偏差，且无需预知因子数量或进行单位根检验。
This paper considers a model with general regressors and unobservable common factors. An estimator based on iterated principal component analysis is proposed, which is shown to be not only asymptotically normal, but under certain conditions also free of the otherwise so common asymptotic incidental parameters bias. Interestingly, the conditions required to achieve unbiasedness become weaker the stronger the trends in the factors, and if the trending is strong enough, unbiasedness comes at no cost at all. The approach does not require any knowledge of how many factors there are, or whether they are deterministic or stochastic. The order of integration of the factors is also treated as unknown, as is the order of integration of the regressors, which means that there is no need to pre-test for unit roots, or to decide on which deterministic terms to include in the model.