The asymptotic behaviour of the residual sum of squares in models with multiple break points
研究了含多个参数断点的最小二乘模型中残差平方和的渐近期望，发现估计断点数和回归参数数对其影响不同，并提出了检验断点位置联合假设的统计量，应用于美国货币政策分析。
Models with multiple discrete breaks in parameters are usually estimated via least squares. This paper, first, derives the asymptotic expectation of the residual sum of squares and shows that the number of estimated break points and the number of regression parameters affect the expectation differently. Second, we propose a statistic for testing the joint hypothesis that the breaks occur at specified points in the sample. Our analytical results cover models estimated by the ordinary, nonlinear, and two-stage least squares. An application to U.S. monetary policy rejects the assumption that breaks are associated with changes in the chair of the Fed.