论文标题
时间序列回归模型中基于残差和WALD型断裂点统计的部分总和过程
Partial Sum Processes of Residual-Based and Wald-type Break-Point Statistics in Time Series Regression Models
论文作者
论文摘要
在测试线性回归模型中的结构断裂时，我们通过获得基于残差和WALD型过程的极限理论来重新审视经典的渐近学。首先，我们建立了这些测试统计数据的布朗桥限制分布。其次，我们研究了非平稳（线性）时间序列回归模型中部分-AM过程的渐近行为。尽管从部分和过程的角度来看，这两个不同的建模环境的特定比较是完成的，但它强调的是，滋扰参数的存在可以改变所考虑的功能的渐近行为。在非组织时间序列回归中测试中断时，模拟实验验证了尺寸扭曲，这表明在这种情况下，布朗桥极限无法提供合适的渐近近似值。需要进一步的研究以在参数稳定性的零假设下建立大小扭曲的原因。
We revisit classical asymptotics when testing for a structural break in linear regression models by obtaining the limit theory of residual-based and Wald-type processes. First, we establish the Brownian bridge limiting distribution of these test statistics. Second, we study the asymptotic behaviour of the partial-sum processes in nonstationary (linear) time series regression models. Although, the particular comparisons of these two different modelling environments is done from the perspective of the partial-sum processes, it emphasizes that the presence of nuisance parameters can change the asymptotic behaviour of the functionals under consideration. Simulation experiments verify size distortions when testing for a break in nonstationary time series regressions which indicates that the Brownian bridge limit cannot provide a suitable asymptotic approximation in this case. Further research is required to establish the cause of size distortions under the null hypothesis of parameter stability.
