面板数据高阶自回归误差Beta回归的联合条件最大似然估计
Joint Conditional Maximum Likelihood Estimation of Beta Regression for Panel Data with Higher-Order Autoregressive Errors
- University of California, San Francisco（旧金山加利福尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对面板Beta回归无法处理高阶自回归依赖的问题，提出联合条件最大似然Beta-AR(k)模型及两种协方差估计器，模拟验证PCSE优于DK，并给出Stata实现。
AI中文摘要：
现有的面板Beta回归估计器无法在联合似然框架内处理高阶自回归依赖。我们开发了这样一个估计器：一个从单一时间序列扩展到面板数据的联合条件最大似然Beta-AR(k)模型，并通过两个新的协方差估计器将其扩展到面板推断——Beck-Katz面板校正标准误（PCSE）的推广和Driscoll-Kraay风格替代方案（DK）——以解决扩展引入的跨面板依赖。该估计器通过蒙特卡洛模拟进行评估，涵盖AR(1)-AR(3)误差结构、三种面板规模和五种序列长度，在独立面板和真实跨面板依赖两种情况下，与未校正基线进行比较。PCSE在I型错误控制和覆盖率方面通常优于DK，其功效损失较小且部分为假象，因为DK自身存在校准不当。在真实跨面板依赖下，未校正估计器的I型错误严重且不随数据增加而减少，而两种校正方法均解决了该问题，其中PCSE再次更受青睐。文中提供了一个多站点疾病管理的示例，以及Stata实现（xtbetark）。
英文摘要：
Existing panel beta regression estimators do not accommodate higher-order autoregressive dependence within a joint likelihood framework. We develop such an estimator: a joint conditional maximum-likelihood beta-AR(k) model extended from a single time series to panel data, and extend it to panel inference through two new covariance estimators - a generalization of Beck-Katz panel-corrected standard errors (PCSE) and a Driscoll-Kraay-style alternative (DK) - to account for the cross-panel dependence the extension introduces. The estimator is evaluated by Monte Carlo simulation spanning AR(1)-AR(3) error structures, three panel sizes, and five series lengths, under both independent panels and genuine cross-panel dependence, against an uncorrected baseline. PCSE generally exhibited superior Type I error control and coverage relative to DK, at a power cost that was modest and partly illusory given DK's own miscalibration. Under genuine cross-panel dependence, the uncorrected estimator's Type I error was severe and did not diminish with additional data, while both corrections resolved it, with PCSE again preferred. An illustrative multi-site disease management example is provided, along with a Stata implementation (xtbetark).
