STAR模型中的递归退势单位根检验研究

在STAR模型框架下，考虑时间序列具有线性确定性趋势成分，本文建立了一个递归退势单位根检验统计量，推导了其渐近分布；并在考虑初始条件情形下，对递归退势、OLS和GLS退势单位根检验统计量的有限样本性质进行了细致的比较研究。若忽略初始条件的影响，GLS退势和递归退势单位根检验统计量的检验势都显著高于OLS退势。随着初始条件的增大，GLS退势单位根检验统计量的检验势下降得比较厉害，递归退势单位根检验统计量的检验势较为稳定，且在样本量较大情形下更具优势。

Unit Root Tests using Recursive De-trending Procedure in STAR Models

This paper focuses on the unit root tests using recursive de-trending procedure against alternative hypothesis where the time series data under investigation follow globally stationary ESTAR processes with alinear deterministic trend. It proposes a unit root test using recursive de-trending method, and derives its asymptotic distribution. The results of comparative study of small sample properties for unit root tests using OLS、GLS and recursive de-trending procedure show that the power of GLS and recursive de-trended unit root tests are better than OLS if the initial condition is negligible. If the initial deviation is sizable, the power of GLS de-trended unit root test declines dramatically, but the recursive de-trended unit root test maintains good power properties, especially for relatively large sample.