A problem with Log Likelihood estimation with artificial data. I need help with this, thank you

[Giuseppe Di Stefano]

Hello everybody,

I'm trying to estimate An, As, Bn and Bs, where An and As are constants, while Bn and Bs are coefficients.
I'm using an artificial dataset and I have thinked to use a Log Likehood estimation, but the function doesn't converg.

The error is:
initial: log likelihood = -<inf> (could not be evaluated)
could not find feasible values


Below I report the do file.

Thank you.
GD

clear
set obs 1000
gen t = _n
tsset t

gen Sn = runiform(10000,30000)
gen Ss = runiform(50000,100000)

scalar An = 5000
scalar As = 10000
scalar Bn = 200
scalar Bs = 30

gen Fn = An + Bn*(Sn-l.Sn) + rnormal(0,600)
gen Fs = As + Bs*(Ss-l.Ss) + rnormal(0,200)

gen F = Fn + Fs

// Fn and Fs are not osservable, and I don't know An, As, Bn, Bs. I osserve only F, Sn ed Ss.

gen Dn = Sn-l.Sn
gen Ds = Ss-l.Ss

prog drop _all
program normal
version 16
args todo b lnf
tempvar mu_Fn sigma_Fn mu_Fs sigma_Fs
mleval `mu_Fn' = `b'
mleval `sigma_Fn' = `b'
mleval `mu_Fs' = `b'
mleval `sigma_Fs' = `b'

// ML Function
quietly replace `lnf' = ln(normalden(($ML_y1 - `mu_Fn')/`sigma_Fn')) - ln(`sigma_Fn') + (ln(normalden(($ML_y2 - `mu_Fs')/`sigma_Fs')) - ln(`sigma_Fs'))

end

ml model lf0 normal (F = Dn) (sigma_Fn (F = Ds) (sigma_Fs
ml max