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Hi, and thanks for this great package. It really helps!
I want to get the n-period ahead forecast in a rolling sense. This means that I need to find the best lambda, make n-period ahead predictions, increase my validation sample (recursive or rolling, this does not matter) by one time period, and re-do the step above.
What would be the most efficient way to get the n-ahead predictions for this scenario? Right now I am thinking about using cv.BigVAR to get the optimal lambda and subsequently re-fit using BigVAR.fit(), before predicting predict(n.ahead=n).
Put differently, I am wondering if my result can be achieved in a one-shot estimation?
The text was updated successfully, but these errors were encountered:
You should be able to accomplish this with a call to cv.BigVAR. You can adjust the forecast horizon by adjusting h in constructModel. The option rolling_oos=TRUE will update the penalty grid over the evaluation period. The out-of-sample predictions are stored in @preds.
Hi, and thanks for this great package. It really helps!
I want to get the n-period ahead forecast in a rolling sense. This means that I need to find the best lambda, make n-period ahead predictions, increase my validation sample (recursive or rolling, this does not matter) by one time period, and re-do the step above.
What would be the most efficient way to get the n-ahead predictions for this scenario? Right now I am thinking about using
cv.BigVAR
to get the optimal lambda and subsequently re-fit usingBigVAR.fit()
, before predictingpredict(n.ahead=n)
.Put differently, I am wondering if my result can be achieved in a one-shot estimation?
The text was updated successfully, but these errors were encountered: