Outliers And Conditional Autoregressive Heteroscedasticity In Time Series

Abstract

This paper reviews the literature on GARCH-type models proposed to represent the dynamic evolution of conditional variances. Effects of level outliers on the diagnostic and estimation of GARCH models are also studied. Both outliers and conditional heteroscedasticity can generate time series with excess kurtosis and autocorrelated squared observations. Consequently, both phenomena can be confused. However, since outliers are generated by unexpected events and the conditional variances are predictable, it is important to identify which one is producing the observed features in the data. We compare two alternative procedures for dealing with the simultaneous presence of outliers and conditional heteroscedasticity in time series. The first one is to clean the series of outliers before fitting a GARCH model. The second is to estimate first the GARCH model and then to clean of outliers by using the residuals adjusted by its conditional variance. It is shown that both approaches may result in different estimated conditional variances.

谢谢啦:)

“时间序列数据在差分得出新的方程后还需要进行异方差,自相关和多重共线性的检验么?”

答：需要。因为：差分一般是为了使序列平稳化，因而差分与序列的非平稳性有关；而上述三个问题（异方差,自相关和多重共线性）很可能是由模型设定所致，因而三个检验与模型设定有关。总之，即使差分后的平稳序列建模很有可能还会出现这三个问题，因而需要检验。

唉，关键是要怎么检验呢？？？？？

还应该再进行检验的。