Hi Sven

with the Script below which is for a time varying parameter. there are 2 things I wish to get from the output of this routine.

First in the final gnuplot command I have added, I want to include in the graph of the dependent variable the estimated/smoothed level of the system with its 95% CI

Second to get a printout of the values of tvar_ bhat and its 95% CI and of the level of the system

Also with regard to StrucTiSM, it would be helpful have a printout of the values of estimated level of the system (and its 95%CI)

Is there some simple script I can add to do these things.? The forecast script you supplied works well thank you in StrucTism post estimation.

cheers

Brian

_________________________________________________________-

function void at_each_step(bundle *b)
b.obsymat = transp(b.mX[b.t,])
end function
open SevernDeeEggs.gdt --quiet
smpl 1998 2019
/* parameter initialization */
scalar b0 = mean(SEVERN_EGGS_UNADJ)
scalar s_obs = 0.1
scalar s_state = 0.1
/* bundle setup */
bundle B = ksetup(SEVERN_EGGS_UNADJ, 1, 1, 1)
matrix B.mX = {time}
matrix B.depvar = {SEVERN_EGGS_UNADJ}
B.timevar_call = "at_each_step"
B.diffuse = 1
set bfgs_maxiter 5000
/* ML estimation of intercept and the two variances */
mle LL = err ? NA : B.llt
B.obsy = B.depvar - b0
B.obsvar = s_obs^2
B.statevar = s_state^2
err = kfilter(&B)
params b0 s_obs s_state
end mle
/* display the smoothed time-varying slope */
ksmooth(&B)
series tvar_b1hat = B.state[,1]
series tvar_b1se = sqrt(B.stvar[,1])
gnuplot tvar_b1hat --time-series --with-lines --output=display \
--band=tvar_b1hat,tvar_b1se,1.96 --band-style=fill
gnuplot SEVERN_EGGS_UNADJ --time-series --with-lines --output=display
____________________________________________________________________

On Wed, 2 Dec 2020 at 16:04, Brian Revell <bjr.newmail@gmail.com> wrote:

Hi Sven
progress - I have managed to get the Script to run with time as indep var -but MLE crashes on reaching max 500 interations . It didn't like the year variable I defined in an earlier run eg 2002, 2003 I think because it was a large magnitude different eg 10^3 to the dependent variable.

So is there a way to increase the max no. of iterations in the MLE routine? see below for output..

? function void at_each_step(bundle *b)
> b.obsymat = transp(b.mX[b.t,])
> end function
? open WELSHEGGSCL.gdt --quiet
Read datafile C:\Users\Brian\Documents\gretl\WELSHEGGSCL.gdt
? smpl 2002 2019
Full data range: 2002 - 2024 (n = 23)
Current sample: 2002 - 2019 (n = 18)

/* parameter initialization */
? scalar b0 = mean(Severnadj)
Generated scalar b0 = 9.84213
? scalar s_obs = 0.1
Generated scalar s_obs = 0.1
? scalar s_state = 0.1
Generated scalar s_state = 0.1
/* bundle setup */
? bundle B = ksetup(Severnadj, 1, 1, 1)
? matrix B.mX = {time}
? matrix B.depvar = {Severnadj}
? B.timevar_call = "at_each_step"
? B.diffuse = 1
/* ML estimation of intercept and the two variances */
? mle LL = err ? NA : B.llt
? B.obsy = B.depvar - b0
? B.obsvar = s_obs^2
? B.statevar = s_state^2
? err = kfilter(&B)
? params b0 s_obs s_state
? end mle
Using numerical derivatives
Tolerance = 1.81899e-012
Reached the maximum iterations (500)

Error executing script: halting
> end mle

On Tue, 1 Dec 2020 at 17:10, Sven Schreiber <svetosch@gmx.net> wrote:

Am 01.12.2020 um 17:20 schrieb Brian Revell:

Hi Sven

many thanks for your prompt feedback. Where did you paste it in? In the Model MLE option from the GUI -or under File -Script File-menu option?

The latter, yes, so basically in the built-in script editor. The MLE area is meant for even lower-level access, without the predefined Kalman filter objects.

know where to do this. The manual is not helpful in this respect.

In later parts of the guide it is pretty much assumed that people know the scripting interface of gretl. However, at the beginning this is explained, please see section 3.1.

(BTW, in my version of the guide the script listing is number 36.4 in ch. 36, I guess you have a slightly older version where some newer chapters do not exist yet. But this shouldn't make a difference in this context.)

cheers

sven

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