请教:时间序列数据在差分后还需要异方差,自相关和多重共线检验么?

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请教: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?

望各位大侠不吝赐教!~谢谢!!

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关键词：时间序列数据 时间序列 序列数据 多重共线 自相关 方差 序列 自相关 差分 共线

In regression analysis using time series, autocorrelation of the residuals is a problem, and leads to an upward bias in estimates of the statistical significance of coefficient estimates, such as the T statistic. The standard test for the presence of autocorrelation is the Durbin-Watson statistic or, if the explanatory variables include a lagged dependent variable, Durbin's h statistic.

Responses to autocorrelation include differencing of the data and the use of lag structures in estimation.

So Answer to your question is YES!

[此贴子已经被作者于2006-5-2 0:22:09编辑过]

谢谢楼上的朋友.
对Time series data来说,好象一般只需要检验autocorrelation和multicollinearity.至于heteroscedasticity虽然也能在Time series data里发生,但一般是针对cross-sectional data.

[此贴子已经被作者于2006-5-2 6:44:07编辑过]

Chen Min and An Hongzhi (1999). A test of conditional heteroscedasticity in time series. Science in China (series A), 41, 26-37. （SCI）

Estimated by the least absolute deviation approach: Diagnostic checking for time series models with conditional heteroscedasticity

Guodong Li and Wai Keung Li
Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong

[此贴子已经被作者于2006-5-2 7:51:12编辑过]

Conditional Heteroscedasticity in Time Series of Stock Returns:

Evidence and Forecasts

Abstract

This article presents new evidence about the time-series behavior of stock prices. Daily return series exhibit significant levels of second-order dependence, and they cannot be modeled as linear white-noise processes. A reasonable return-generating process is empirically shown to be a first-order autoregressive process with conditionally heteroskedastic innovations. In particular, generalized autoregressive conditional heteroskedastic GARCH (1, 1) processes fit to data very satisfactorily. Various out-of-sample forecasts of monthly return variances are generated and compared statistically. Forecasts based on the GARCH model are found to be superior. Copyright 1989 by University of Chicago Press.

[此贴子已经被作者于2006-5-2 7:47:28编辑过]

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？？？？？