Dear all,
The script below
illustrate the problem
Findings: extremely bad lnl
in comparison to --x-12-arima
zero values of the 2 last parameters
and gradients at all iterations
Strangely large scaling factor
Note that --x-12-arima gives
nice pol. roots and excellent Ljung-Box Q'

Also note that with the default $pd for
modtest --autocorr
obviously fails because zero df


open bad_data.gdt #attached
smpl 1 194
# note strange zeros for b[y_one] and b[y_two]
arima 3 0 0; 1 0 0; diff_series const y_one y_two  
lnl1 = $lnl
modtest --autocorr 5
# compare

arima 3 0 0; 1 0 0; diff_series const y_one y_two --x-12-arima
lnl2 = $lnl
modtest --autocorr 5
eval lnl2 - lnl1

arima 3 0 0; 1 0 0; diff_series const y_one y_two --verbose

# Scaling y by 2.18989e+018 !!!

/*
Iteration 1: loglikelihood = -8292.74884180
Parameters: -2.8152e+016     0.65496   -0.090036    -0.16211    -0.44328     0.00000
                0.00000
Gradients:   4.7608e-018      4.0881      1.2538      3.3817     -1.2644     0.00000
                0.00000 (norm 7.59e-001)

Iteration 2: loglikelihood = -8292.71644305 (steplength = 0.0016)
Parameters: -2.8152e+016     0.66150   -0.088030    -0.15670    -0.44531     0.00000
                0.00000
Gradients:   4.5624e-018      1.3507     -1.5003      1.0847     -1.4772     0.00000
                0.00000 (norm 5.32e-001)

*/

#etc. etc

Oleh