【主讲】Hou Jie 助教授 (首都经贸大学)
【主题】The Pivotal Test Statistic for Joint Segmented Trend Break under Heterogeneous Innovations
【时间】2014年10月23日 (周四) 15：30-17：00
【地点】上海财经大学经济学院楼602室
【语言】英文
【摘要】We generalize the joint segmented trend break test in Perron and Zhu (2005) to the case of series with heterogenous innovation. the innovation (idiosyncratic error) process is assumed to have a time-varying variance that covers almost all types of innovations that are not unit-root or fractionally integrated processes, which can also be included in our framework with a slight modification of argument. We argue that with heterogenous innovation OLS excels MLE in both estimation and testing. We also give a very general condition under which a pivotal statistic for coefficient break (e.g. trend break or mean shift) test is guaranteed to exist even with time-varying innovations. This result is relevant to the literature of extreme value theory (EVT) in the sense that its key assumption, that the maximization has to be taken over a set of "strongly correlated" random variables, is exactly the opposite of the key assumption in EVT that the maximization is taken over a set of independent random variables. Moreover, we show that our result still holds if allowing for statistics without finite distribution before rescaling, hence includes EVT on normal variables as special case. We also point out that our results can be extended from scaling transformations to any invertible linear transformations. Simulation results are reported to illustrate the finite sample performance of results obtained.
