Rolling approaches (also known as rolling regression, recursive regression or reverse recursive regression) are often used in time series analysis to assess the stability of the model parameters with respect to time.
A common assumption of time series analysis is that the model parameters are time-invariant. However, as the economic environment often changes, it may be reasonable to examine whether the model parameters are also constant over time. One technique to assess the constancy of the model parameters is to compute the parameter estimates over a rolling window with a fixed sample size through the entire sample. If the parameters are truly constant over the entire sample, then the rolling estimates over the rolling windows will not change much. If the parameters change at some point in the sample, then the rolling estimates will show how the estimates have changed over time.
EViews does not have an extensive rolling regression functionality built-in, but it does offer different ways to perform rolling regressions:
Write an EViews program: we can estimate an equation for each sample in the roll, and then save the results. The following EViews forum posts provided detailed examples.
Basic Rolling Regression
Rolling Multiple Regression
Stepwise rolling regression: plot of coefficients
GARCH - rolling regressions
Rolling VAR estimates
You can also find more detailed examples of rolling regression under your Help menu in EViews. Go to: Help/Quick Reference/Sample Programs & Data/ then click the roll link for detailed examples.
The "Roll" Add-In is a simple EViews program that is integrated into EViews, allowing you to execute the rolling regression program from a single equation object.
Use the EViews rolling regression User Object: EViews allows us to create a new roll object and store various coefficients or statistics from each iteration of the roll.