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Hi Tom,
I'm wondering how can i change the title of this topic :maximum likelihood estimators to VAR study?

Also, according this task below:

[quote][/quote]Use the LS estimate and compute point forecasts ^
y86(1),^
y86(2) (that is, the
forecast origin is the last quarter of 1968) and the corresponding MSE matrices

^Σy(1),^
Σy(2),
^Σ^
y(1), and
^Σ
^y(2). Use these estimates to set up approximate
95% interval forecasts assuming that the process yt is Gaussian.

Where can i get some interesting explanations about how to compute estimated forecast MSE matrices? for h=1 it's easy but as you know the tricky point is for h=2 when i need to compute the omega matrix like in the Lütkepohl's book: New Introduction To Multiple Time Series Analysis p.99?

Thanks Tom

Nabtheberliner wrote:Hi Tom,
I'm wondering how can i change the title of this topic :maximum likelihood estimators to VAR study?

Start a new topic (which I already did). Don't rename as existing one since anyone looking for information on what you were doing before won't find it otherwise.

Nabtheberliner wrote:Also, according this task below:

Use the LS estimate and compute point forecasts ^
y86(1),^
y86(2) (that is, the
forecast origin is the last quarter of 1968) and the corresponding MSE matrices

^Σy(1),^
Σy(2),
^Σ^
y(1), and
^Σ
^y(2). Use these estimates to set up approximate
95% interval forecasts assuming that the process yt is Gaussian.

Where can i get some interesting explanations about how to compute estimated forecast MSE matrices? for h=1 it's easy but as you know the tricky point is for h=2 when i need to compute the omega matrix like in the Lütkepohl's book: New Introduction To Multiple Time Series Analysis p.99?

Thanks Tom

I would recommend not bothering with that. So-called "exact" calculations of standard errors for IRF's and forecasts was one of Lütkepohl's research interests, and it shows in the book. As you can see, it involves quite a bit of number-crunching. A more common way to compute the distribution of forecasts taking into account uncertainty in the coefficient estimates is with Monte Carlo integration.

OK sorry Tom you were very quick, it wasn't gone.
Thanks for the change

OK i skip the question