Forecast standard errors

[paroster]

Hi,

It seems to me that the forecast standard errors you get when forecasting a series that is covariance stationary once it has been transformed by taking the percentage change are incorrect. For example, if you generate forecasts for GDP growth using the AR(1) model

@pc(gdp) c @pc(gdp(-1))

the forecast standard errors do not converge to a constant value for large enough horizon (the way we expect them to). (That they grow with the forecast horizon for the level of GDP is of course fine and in line with theory for a unit-root process.)

However, if you transform the series manually - that is, generate the series x which is the percentage change of GDP - and then model x such as

x c x(-1)

the forecast standard errors are correct.

Would you agree?

[EViews Gareth]

Which version of EViews are you using, and what is the build date? (Help->EViews)

[paroster]

I'm running Eviews 8 (May 6, 2013)

[EViews Gareth]

ok, we'll look into it (might take a while).

[EViews Gareth]

We're stilling looking at this (it is taking a while!).

[EViews Gareth]

The latest patch tweaked things a little to improve this.