![]() ![]() ![]() statistics, crosstab, correlations, covariances, correlogram, cross correlation. So if you regard the major possibilities as either the series being I(1) or low-to-moderate order stationary ARMA, then the ACF can sometimes be of some help in distinguishing them (but you'd typically difference and look again before saying too much). This is a short guide to use EViews, the econometric software that we. This correlogram provides a rough check of asymmetry in the ARCH effect. is computed by an auxiliary regression of the squared residuals on all possible cross. The third line creates a group and the fourth line plots the cross correlogram squared standardized residual and the standardized residual, up to 24 leads and lags. Users Guide, p 159) If there is no serial correlation. You will also tend to see it with $I(2)$ (etc) series, but data that's reasonably modelled by $I(1)$ or is at least stationary after differencing tends to be more common. The first line estimates a GARCH(1,1) model and the second line retrieves the standardized residuals. If you have an $I(1)$ series (a very specific kind of nonstationarity), you should see an ACF that doesn't exhibit the kind of geometric "decay" in the characteristic manner that you see with data generated by typical lowish-order stationary ARMA. The quote in your comment claims too much but does relate to something real, and that something can be useful in figuring out suitable models for data. ![]()
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