We estimate and test for multiple structural breaks in distribution via an empirical characteristic function approach. By minimizing the sum of squared generalized residuals, we can consistently estimate the break fractions. We propose a sup-F type test for structural breaks in distribution as well as an information criterion and a sequential testing procedure to determine the number of breaks. We further construct a class of derivative tests to gauge possible sources of structural breaks, which is asymptotically more powerful than the smoothed nonparametric tests for structural breaks. Simulation studies show that our method performs well in determining the appropriate number of breaks and estimating the unknown breaks. Furthermore, the proposed tests have reasonable size and excellent power in finite samples. In an application to exchange rate returns, our tests are able to detect structural breaks in distribution and locate the break dates. Our tests also indicate that the documented breaks appear to occur in variance and higher-order moments, but not so often in mean.

我们通过经验特征函数方法估计和测试分布中的多个结构中断。通过最小化广义残差平方和，我们可以一致地估计中断分数。我们提出了一个 sup- F分布结构中断的类型测试以及信息标准和顺序测试程序以确定中断的数量。我们进一步构建了一类导数测试来衡量结构断裂的可能来源，这在渐近程度上比结构断裂的平滑非参数测试更强大。模拟研究表明，我们的方法在确定适当的中断次数和估计未知中断方面表现良好。此外，所提出的测试在有限样本中具有合理的规模和出色的功效。在汇率回报的应用中，我们的测试能够检测分布中的结构性中断并找到中断日期。我们的测试还表明，记录的中断似乎出现在方差和高阶矩中，但并不经常出现在均值中。