Breaks in persistence in fixed-T panel data
研究了自回归面板数据模型中自回归系数发生结构断点的情况，提出了基于最小二乘的估计量，在截面单位数N大、时间期数T小时一致，可快速检测新阶段的开始。
This paper considers an autoregressive panel data model in which the autoregressive coefficient has undergone a structural break. The object of interest is the unknown breakpoint. A least squares-based estimator is proposed that is shown to be consistent when only the number of cross-section units, N, is large and the number of time periods, T, is small, thereby enabling quick detection of the onset of a new regime.