Previous comparisons of ordinary least squares with Newey-West standard errors (OLS-NW) and Prais-Winsten (PW) regression in multiple-group interrupted time series analysis have been limited to first-order autoregressive (AR[1]) errors because PW estimation for higher-order AR[k] processes was previously unavailable. We conducted the first systematic evaluation of OLS-NW and PW under AR[2] and AR[3] error structures using Monte Carlo simulation. Simulations examined mild positive, oscillatory, and high persistent autocorrelation across varying series lengths and effect sizes. OLS-NW generally showed higher apparent power but substantially inflated Type I error and poor coverage, particularly under persistent autocorrelation, where inferential performance worsened with increasing AR order and series length. PW maintained substantially better inferential calibration across nearly all conditions. Both methods were approximately unbiased.

翻译：先前在多重分组中断时间序列分析中，普通最小二乘法结合Newey-West标准误（OLS-NW）与Prais-Winsten（PW）回归的比较研究仅限于一阶自回归（AR[1]）误差，这是因为针对高阶AR[k]过程的PW估计此前无法实现。本研究首次通过蒙特卡洛模拟，系统评估了AR[2]和AR[3]误差结构下OLS-NW与PW方法的性能。模拟实验考察了轻度正相关、振荡性相关及高度持久性自相关场景，涵盖不同序列长度与效应量。结果表明：OLS-NW通常表现出更高的表观检验效能，但其第一类错误率显著膨胀且覆盖率较差；尤其在持久性自相关条件下，随着AR阶数与序列长度的增加，其推断性能愈发恶化。而PW方法在几乎所有条件下均保持了显著更优的推断校准性。两种方法的估计近似无偏。