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Hi Dear Eviews users,

I was wondering if there is any Add-ins package in the Eviews 8 that can be used for recursive regression or if I can change settings of Rolling regression in Add-ins package of the Eviews to get the recursive regression result.

Thanks

How are you defining recursive regression?

Thanks a lot for your quick replay. I have 233 monthly observations for two series of variables (one independant variable: X and one dependant variable: R).My regression is R(t)= c1+c2 X(t-1). I've used 153 first observations to estimate in-sample results. I need to do one step ahead forecast as out-of-sample results, which means in a first time I should use the data from the beginning of the time series until observation number 154 to forecast R then used the data from the beginning to the observation 155 to do second out-of-sample forecast and so on to the end of the 233 observations(repeat for 79 times). As I understood if the beginning of the forecast period is constant and the forecast period expands continuously, it's a recursive regression. However in rolling regression the beginning of forecast period is changed in every forecast period).

That sounds like rolling regression, but anchored at start

Yes, you are right. Just installed advance rolling Add-ins and found there is an option to fix the start point. Anyway, thank you very much for your time.

I have another question. I've used the advance rolling regression from Add-ins to estimate my regressions. When the results appeared in the summary table in the link "proc" above that table another link "forecast" is now active. I'm confused now, I needed to do one step ahead forecast for my out-of-sample. The rolling regression that I've already used by assuming anchored at start is not forecasting? Do I need to use this forecast link after doing rolling regression for out-sample part of my period? I used that forecast link, but it just provide me a diagram. I really appreciate your help.

The rolling regression add-in doesn't perform forecasting.

Hi Gareth,

I've changed the rolling regression program that I found in the forum a little bit and used it for forecasting one period ahead out-of-sample forecast (I needed to do recursive regression).Then, for checking that my program is correct I've done manually one period ahead forecast for 24 observations and I found forecasted values are slightly different from those ones that I got from running of program in Eviews. Is that alright to see a little bit different (e.g. forecasted excess return of running program is 0.004263394, and manually forecasted one is:0.003012515).

Thanks a lot for all your helps and hints inadvance.

This is the program that I run:


'run recursive regression

' set window size
!window= 153

' set step size
!step = 1

' get size of workfile
!length = @obsrange

' declare equation for estimation
equation dyinsample

'calculate number of itteration
!nIter = @floor((!length-!window)/!step)

'matrix to store coefficient estimates
matrix(2,!nIter) coefmat ' where 2 is the number of coefficients

'series to store forecast estimates
series fcast
series resid1

'*EDITED: catching start and end points
%start = "@first" '@otod(@ifirst(ser))
%end = "@last" '@otod(@ilast(ser))

' move sample !step obs at a time
for !i = 1 to !length-!window+1-!step step !step

' set sample for estimation period
%first = @otod(@dtoo(%start))
%last = @otod(@dtoo(%start)+!i+!window-2)
smpl {%first} {%last}

' estimate equation - where the equation is r =c(1) + c(2)*dy(-1)
dyinsample.ls r c dy(-1)

' store coefficients
colplace(coefmat,dyinsample.@coefs,!i)

' 1-period-ahead forecast
%1pers = @otod(@dtoo(%start)+!i+!window-1) 'start point
%1pere = @otod(@dtoo(%start)+!i+!window) 'end point: %1pere - %1pers +1 = 1 '*EDITED
if @dtoo(%end) < @dtoo(%1pere) then 'check whether the forecast end point is greater than the workfile end point
exitloop
endif

' set smpl for forecasting period
smpl {%1pers} {%1pere}

' forecast with command *forecast* (see also *fit*)
dyinsample.forecast(f=na) yf


' set sampl to obtain the 1th period observation
smpl {%1pere} {%1pere}

' store forecasts
fcast = yf
resid1 = r - fcast
next

smpl @all
show coefmat
show fcast.line

d(noerr) yf

No, they should be identical.