Dear all,
again processor-detecting dependent behavior in arima

1) script

eval $sysinfo
open bad_data.gdt #attached
smpl 1 194
series sty=diff_series/sd(diff_series)
list zli = y_one y_two
y = sty+6.48
arima 3 0 0; 1 0 0; y 0 zli --verbose

Note: on the same pc and os blascore = Prescottl; blascore = Atom

2) pc info
    Motherboard:
      CPU Type                                          QuadCore Intel Pentium N3540, 2666 MHz (32 x 83)
      Motherboard Name                                  Lenovo B50-10
      Motherboard Chipset                               Intel Bay Trail-M
      System Memory                                     3978 MB
      DIMM1: SK hynix HMT451S6BFR8A-PB                  4 GB DDR3-1600 DDR3 SDRAM  (11-11-11-28 @ 800 MHz)  (10-10-10-27 @ 761 MHz)  (9-9-9-24 @ 685 MHz)  (8-8-8-22 @ 609 MHz)  (7-7-7-19 @ 533 MHz)  (6-6-6-16 @ 457 MHz)  (5-5-5-14 @ 380 MHz)
      BIOS Type                                         Unknown (04/14/2015)
      Communication Port                                Последовательный порт (COM1)

3) output 1, system installed
# Output 1, 2018d-git,  wordlen = 64
gretl version 2018d-git
Current session: 2018-10-26 14:48

? eval $sysinfo
bundle anonymous:
 nproc = 4
 blascore = Prescott
 hostname = DESKTOP-DE5ESQO
 os = windows
 mpi = 0
 blas = openblas
 omp_num_threads = 4
 omp = 1
 blas_parallel = OpenMP
 mpimax = 4
 wordlen = 64

? open bad_data.gdt

Read datafile C:\Users\Lenovo\Documents\gretl\bad_data.gdt
periodicity: 4, maxobs: 204
observations range: 1950:1 to 2000:4

Listing 4 variables:
 0) const          1) diff_series    2) y_one          3) y_two        

? smpl 1 194
Full data range: 1950:1 - 2000:4 (n = 204)
Current sample: 1950:1 - 1998:2 (n = 194)

? series sty=diff_series/sd(diff_series)
Generated series sty (ID 4)
? list zli = y_one y_two
Generated list zli
? y = sty+6.48
Generated series y (ID 5)
? arima 3 0 0; 1 0 0; y 0 zli --verbose
NLS: failed to converge after 1605 iterations

Error executing script: halting
> arima 3 0 0; 1 0 0; y 0 zli --verbose

4) output 2 the same pc and os, 2018c, portable

gretl version 2018c
Current session: 2018-10-26 14:50

? eval $sysinfo
bundle anonymous:
 nproc = 4
 blascore = Atom
 hostname = DESKTOP-DE5ESQO
 os = windows
 mpi = 0
 blas = openblas
 omp_num_threads = 4
 omp = 1
 blas_parallel = OpenMP
 mpimax = 4
 wordlen = 32

? open bad_data.gdt

Read datafile C:\Users\Lenovo\Documents\gretl\bad_data.gdt
periodicity: 4, maxobs: 204
observations range: 1950:1 to 2000:4

Listing 4 variables:
 0) const          1) diff_series    2) y_one          3) y_two        

? smpl 1 194
Full data range: 1950:1 - 2000:4 (n = 204)
Current sample: 1950:1 - 1998:2 (n = 194)

? series sty=diff_series/sd(diff_series)
Generated series sty (ID 4)
? list zli = y_one y_two
Generated list zli
? y = sty+6.48
Generated series y (ID 5)
? arima 3 0 0; 1 0 0; y 0 zli --verbose

ARMA initialization: using nonlinear AR model

Iteration 1: loglikelihood = -136.938951717
Parameters:       6.4637     0.58059    -0.11046   -0.082556   -0.093679     0.10601
                -1.9068
Gradients:        9.2712      10.471      1.3218     -2.0031     -2.1470      3.9715
                -2.8401 (norm 3.22e+000)

Iteration 2: loglikelihood = -136.689823002 (steplength = 0.0016)
Parameters:       6.4785     0.59734    -0.10835   -0.085761   -0.097114     0.11237
                -1.9113
Gradients:        3.7130      3.7662     -2.9409     -3.1750     -1.7423     -1.1818
                -2.5919 (norm 2.14e+000)

Iteration 3: loglikelihood = -136.635783208 (steplength = 0.0016)
Parameters:       6.4839     0.60234    -0.11626   -0.092855    -0.10056     0.10774
                -1.9166
Gradients:        1.9617      4.8733      1.0840      1.5885      1.2310      3.6716
               -0.80868 (norm 1.60e+000)

Iteration 4: loglikelihood = -136.600859241 (steplength = 0.008)
Parameters:       6.4784     0.61437    -0.13175   -0.090207   -0.091373     0.11082
                -1.9254
Gradients:        3.9294      3.0305      2.2406      1.5760     0.43245      2.7506
               -0.97313 (norm 2.07e+000)

Iteration 5: loglikelihood = -136.590682758 (steplength = 0.008)
Parameters:       6.4844     0.61344    -0.13942   -0.082111   -0.082786     0.11045
                -1.9365
Gradients:        1.7762      4.1148      3.1480     0.21233    -0.75300      3.1392
                -1.2209 (norm 1.57e+000)

Iteration 6: loglikelihood = -136.579257175 (steplength = 0.008)
Parameters:       6.4832     0.61301    -0.13869   -0.079072   -0.080908     0.11078
                -1.9511
Gradients:        2.1798      3.8598      2.7328    -0.11127    -0.24403      3.3903
               -0.55423 (norm 1.62e+000)

Iteration 7: loglikelihood = -136.558904481 (steplength = 0.04)
Parameters:       6.4846     0.59869    -0.13673   -0.098319   -0.057675     0.13257
                -1.9918
Gradients:        1.7338      3.3469      1.7212     0.78241     -1.7498      3.2189
               -0.55661 (norm 1.47e+000)

Iteration 8: loglikelihood = -136.521260314 (steplength = 0.04)
Parameters:       6.4876     0.56033    -0.15574    -0.11922   -0.052945     0.20112
                -2.0607
Gradients:       0.32695      2.4407     0.16140     -1.6302     -2.7718     -1.1861
                -1.7788 (norm 1.05e+000)

Iteration 9: loglikelihood = -136.495894655 (steplength = 1)
Parameters:       6.4896     0.58297    -0.15220    -0.11017   -0.066710     0.18134
                -2.0469
Gradients:      -0.43210    -0.65107    -0.17364     0.15633     0.59707     0.32961
                0.36414 (norm 7.63e-001)

Iteration 10: loglikelihood = -136.490592511 (steplength = 1)
Parameters:       6.4886     0.57213    -0.15608    -0.11690   -0.061996     0.19671
                -2.0648
Gradients:     -0.097512    -0.22807    -0.23333    -0.23077   -0.058053    -0.28839
              -0.074467 (norm 3.86e-001)

Iteration 11: loglikelihood = -136.490080201 (steplength = 1)
Parameters:       6.4884     0.56792    -0.15787    -0.11988   -0.060088     0.20265
                -2.0736
Gradients:     -0.026271   -0.042462    -0.11705    -0.17211    -0.13359    -0.25929
               -0.11138 (norm 2.74e-001)

Iteration 12: loglikelihood = -136.489973117 (steplength = 1)
Parameters:       6.4883     0.56748    -0.15809    -0.12036   -0.059698     0.20303
                -2.0754
Gradients:      0.014754    0.015626   -0.015933   -0.033397   -0.049127   -0.037086
              -0.030232 (norm 1.62e-001)

Iteration 13: loglikelihood = -136.489964277 (steplength = 1)
Parameters:       6.4883     0.56727    -0.15829    -0.12056   -0.059753     0.20348
                -2.0763
Gradients:    -0.0068714  -0.0023991 6.6724e-006  0.00028924   0.0033536  -0.0024461
             0.00092363 (norm 8.33e-002)

Iteration 14: loglikelihood = -136.489964105 (steplength = 1)
Parameters:       6.4883     0.56731    -0.15825    -0.12052   -0.059747     0.20339
                -2.0762
Gradients:     0.0010810-8.4809e-005 1.7192e-005  0.00019304  0.00019005  0.00043713
             0.00024676 (norm 3.32e-002)

Iteration 15: loglikelihood = -136.489964103 (steplength = 1)
Parameters:       6.4883     0.56731    -0.15825    -0.12053   -0.059747     0.20339
                -2.0762
Gradients:   -0.00012818 5.0844e-005 2.7890e-005 2.0559e-005-3.7774e-005 2.5363e-005
           -3.0714e-005 (norm 1.16e-002)

Iteration 15: loglikelihood = -136.489964103 (steplength = 1)
Parameters:       6.4883     0.56731    -0.15826    -0.12053   -0.059747     0.20339
                -2.0762
Gradients:   -0.00012818 5.0844e-005 2.7890e-005 2.0559e-005-3.7774e-005 2.5363e-005
           -3.0714e-005 (norm 1.16e-002)

--- FINAL VALUES:
loglikelihood = -136.489964103 (steplength = 5.12e-007)
Parameters:       6.4883     0.56731    -0.15826    -0.12053   -0.059747     0.20339
                -2.0762
Gradients:   -0.00012818 5.0844e-005 2.7890e-005 2.0559e-005-3.7774e-005 2.5363e-005
           -3.0714e-005 (norm 1.16e-002)

Function evaluations: 47
Evaluations of gradient: 15

Model 1: ARMAX, using observations 1950:1-1998:2 (T = 194)
Estimated using AS 197 (exact ML)
Dependent variable: y
Standard errors based on Hessian

            coefficient   std. error      z       p-value
 ---------------------------------------------------------
 const       6.48832      0.0467055    138.9      0.0000   ***
 phi_1       0.567313     0.159434       3.558    0.0004   ***
 phi_2      −0.158255     0.107503      −1.472    0.1410  
 phi_3      −0.120526     0.130267      −0.9252   0.3549  
 Phi_1      −0.0597470    0.121899      −0.4901   0.6240  
 y_one       0.203395     0.233933       0.8695   0.3846  
 y_two      −2.07615      0.389196      −5.334    9.58e-08 ***

Mean dependent var   6.480000   S.D. dependent var   1.000000
Mean of innovations −0.001117   S.D. of innovations  0.488410
Log-likelihood      −136.4900   Akaike criterion     288.9799
Schwarz criterion    315.1228   Hannan-Quinn         299.5659

                       Real  Imaginary    Modulus  Frequency
 -----------------------------------------------------------
 AR
   Root  1           1.0476    -1.1562     1.5602    -0.1328
   Root  2           1.0476     1.1562     1.5602     0.1328
   Root  3          -3.4083     0.0000     3.4083     0.5000
 AR (seasonal)
   Root  1         -16.7372     0.0000    16.7372     0.5000
 -----------------------------------------------------------


Oleh