NONPARAMETRIC TIME-VARYING PANEL DATA MODELS WITH HETEROGENEITY
改进了交互固定效应面板数据模型的迭代算法，允许回归系数随个体和时间变化，并用核范数惩罚和双最小二乘迭代证明了理论性质与有限样本表现。
Since Bai (2009, Econometrica 77, 1229–1279), considerable extensions have been made to panel data models with interactive fixed effects (IFEs). However, little work has been conducted to understand the associated iterative algorithm, which, to the best of our knowledge, is the most commonly adopted approach in this line of research. In this paper, we refine the algorithm of panel data models with IFEs using the nuclear-norm penalization method and duple least-squares (DLS) iterations. Meanwhile, we allow the regression coefficients to be individual-specific and evolve over time. Accordingly, asymptotic properties are established to demonstrate the theoretical validity of the proposed approach. Furthermore, we show that the proposed methodology exhibits good finite-sample performance using simulation and real data examples.