Timing structural change: a conditional probabilistic approach
提出一种基于似然的方法，为时间序列中结构断点发生在不同日期分配条件概率，从而提高推断变化时机的精度，并通过蒙特卡洛实验和美国GDP增长率波动下降的实例验证。
Abstract We propose a strategy for assessing structural stability in time‐series frameworks when potential change dates are unknown. Existing stability tests are effective in detecting structural change, but procedures for identifying timing are imprecise, especially in assessing the stability of variance parameters. We present a likelihood‐based procedure for assigning conditional probabilities to the occurrence of structural breaks at alternative dates. The procedure is effective in improving the precision with which inferences regarding timing can be made. We illustrate parametric and non‐parametric implementations of the procedure through Monte Carlo experiments, and an assessment of the volatility reduction in the growth rate of US GDP. Copyright © 2006 John Wiley & Sons, Ltd.