🤖 AI Summary
本文研究了具有个体特异性系数和灵活误差结构的动态面板数据模型，提出了一种基于逆Radon变换的多步估计方法以解决随机系数分布及误差密度估计问题。
📝 Abstract
We study dynamic linear panel data models in which the lagged outcomes and strictly exogenous covariates carry individual-specific coefficients and the time-varying errors have a flexible covariance structure. With a fixed number of time periods, we point-identify the joint distribution of the random coefficients and the structural errors under a distributional form of strict exogeneity, and propose a closed-form, multi-step estimator based on the inverse Radon transform. We establish a uniform convergence rate for the estimator of the random coefficient density, as well as uniform consistency of the estimator for the conditional density of the time-varying structural errors. Monte Carlo simulations demonstrate good finite-sample performance of the estimators.