Testing For and Dating Common Breaks in Multivariate Time Series
提出了在多元时间序列中为单一共同断点构建渐近有效置信区间的方法，适用于I(0)、I(1)和确定性趋势回归量。置信区间宽度随序列数增加而减小，并通过欧洲三国产出增长率和美国消费、投资、产出增长率的实例验证。
This paper develops methods for constructing asymptotically valid confidence intervals for the date of a single break in multivariate time series, including I(0), I(1), and deterministically trending regressors. Although the width of the asymptotic confidence interval does not decrease as the sample size increases, it is inversely related to the number of series which have a common break date, so there are substantial gains to multivariate inference about break dates. These methods are applied to two empirical examples: the mean growth rate of output in three European countries, and the mean growth rate of U.S. consumption, investment, and output.