AI 中文总结
该研究针对时间期数固定的动态线性面板数据模型，提出基于逆拉东变换的闭式多步估计量，通过蒙特卡洛模拟验证了其良好的有限样本表现，实现了随机系数与结构误差联合分布的点识别。
AI 中文摘要
我们研究动态线性面板数据模型，其中滞后结果和严格外生协变量具有个体特定系数，时变误差具有灵活的协方差结构。在时间期数固定的情况下，我们在严格外生性的分布形式下点识别随机系数与结构误差的联合分布，并提出基于逆拉东变换的闭式多步估计量。我们确定了随机系数密度估计量的一致收敛速率，以及时变结构误差条件密度估计量的一致一致性。蒙特卡洛模拟表明该估计量具有良好的有限样本表现。
英文摘要
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.
Comments45 pages
