In theory, I don't see why that wouldn't work. However, I would imagine it both more tedious and error-prone to writing a program that computes the forecast series for you.Yes, the difference between a rolling forecast and a recursive forecast is clear for me (although I didn't manage to express myself very wellA rolling forecast is similar to a recursive forecast but different in that the recursive forecast uses an expanding window instead of a rolling window. However, if someone has written a program that does rolling forecasts it should be very easy to change it a bit to do recursive forecasts instead.). And the link Gareth provided will surely be of great help:)
But do you know if the method described above (just collecting the recursive coefficients provided by an in-sample regression) will provide the correct forecasts?
Again, thank you for all the help
Hi,Thank you for the answer, I will definetely spend some time trying to implement that basic rolling regression program. I'm sorry that the explanation of recursive forecasting is not the most precise, so I refer to the explanation in Brooks' book "Introductory Econometrics for Finance"You might find Esther's program here:
http://forums.eviews.com/viewtopic.php?f=15&t=878
useful. It performs rolling forecasts, which is, I think, what you're describing.
… a useful way around this problem is to use a recursive or rolling window, which generates a series of forecasts for a given number of steps ahead. A recursive forecasting model would be one where the initial estimation date is fixed, but additional observations are added one at a time to the estimation period. A rolling window, on the other hand, is one where the length of the in-sample period used to estimate the model is fixed, so that the start date and end date successively increase by one observation. Suppose now that only one-step-ahead forecasts are of interest. They could be produced using the following recursive and rolling window approaches:
Objective: to produce.....................................Data used to estimate model parameters
1-step-ahead forecasts for:......................Rolling window.................Recursive window
1999M1............................................1990M1--1998M12 ...............1990M1--1998M12
1999M2............................................1990M2--1999M1.................1990M1--1999M1
1999M3............................................1990M3--1999M2.................1990M1--1999M2
1999M4............................................1990M4--1999M3.................1990M1--1999M3
1999M5............................................1990M5--1999M4.................1990M1--1999M4
1999M6............................................1990M6--1999M5.................1990M1--1999M5
1999M7............................................1990M7--1999M6.................1990M1--1999M6
1999M8............................................1990M8--1999M7.................1990M1--1999M7
1999M9............................................1990M9--1999M8.................1990M1--1999M8
I hope this illustration makes my previos posts a little clearer.
Code: Select all
' set sample for estimation period
%first = @otod(@dtoo(%start)+!i-1)
%last = @otod(@dtoo(%start)+!i+!window-2)
smpl {%first} {%last}
Code: Select all
' set sample for estimation period
%first = @otod(@dtoo(%start))
%last = @otod(@dtoo(%start)+!i+!window-2)
smpl {%first} {%last}