This post is an accident. Please ignore it.

> Alexios,

>

> I updated my script to use multiple solvers and compare results.

>

> It looks like nlminb is the most accurate solver for this generic dataset

> that I am using.

>

> I posted the results on stack exchange so its easier to view the results.

>

> If you want, I can post the results here.

>

> Here is the url with the updated script and results:

>

>

>

https://stackoverflow.com/questions/51900177/should-the-positioning-of-the-external-regressors-change-the-output-of-arma-garc/>

>

> On Mon, Aug 20, 2018, 7:29 AM GALIB KHAN <

[hidden email]>

> wrote:

>

>> Alexios,

>>

>> I changed the solver to "hybrid" but kept the scaling that you provided

>> and got different results when switching the positions for the external

>> regressors.

>>

>> ugarchfit(spec = spec, data = as.matrix(temp$y),solver =

>> "hybrid",fit.control=list(scale=1))

>>

>> So it looks like you have to change the solver to "nlminb" and update

>> fit.control with the scaling that you provided.

>>

>> Galib

>>

>>

>> On Sun, Aug 19, 2018 at 11:41 PM, GALIB KHAN <

>>

[hidden email]> wrote:

>>

>>> Alexios,

>>>

>>>

>>> Veryyyy interesting!!!!! No I cannot see any differences at all lol.

>>>

>>> I updated the code and indeed you are correct sir. Thank you for your

>>> time in investigating this.

>>>

>>> I will update my stack exchange post to reflect your answer in the

>>> morning.

>>>

>>> Again thank you for all your help!!!!

>>>

>>> Best,

>>> Galib Khan

>>>

>>>

>>> On Sun, Aug 19, 2018 at 11:19 PM, alexios galanos <

[hidden email]>

>>> wrote:

>>>

>>>> I did use the seed you provided.

>>>>

>>>> Use the following code for estimation:

>>>>

>>>> fit <- ugarchfit(spec = spec, data = as.matrix(temp$y),solver =

>>>> "nlminb", fit.control=list(scale=1))

>>>>

>>>> model_maker(var1)

>>>> Estimate Std. Error t value Pr(>|t|)

>>>> mu -7.3998577 0.69086641 -10.7109821 0.0000000000

>>>> ar1 0.3387323 0.08280162 4.0908900 0.0000429721

>>>> ar2 -0.8834201 0.06569477 -13.4473414 0.0000000000

>>>> ma1 -0.2902069 0.08598589 -3.3750525 0.0007380161

>>>> ma2 0.8660807 0.06778418 12.7770320 0.0000000000

>>>> mxreg1 1.6782992 0.12769644 13.1428825 0.0000000000

>>>> mxreg2 2.5225382 0.04292728 58.7630625 0.0000000000

>>>> omega 12.0047145 0.82986864 14.4658010 0.0000000000

>>>> alpha1 0.0000000 0.07358520 0.0000000 1.0000000000

>>>> shape 63.0103309 98.49188643 0.6397515 0.5223341761

>>>>

>>>> model_maker(var2)

>>>> Estimate Std. Error t value Pr(>|t|)

>>>> mu -7.3998549 0.69086651 -10.7109764 0.000000e+00

>>>> ar1 0.3387334 0.08280150 4.0909088 4.296861e-05

>>>> ar2 -0.8834206 0.06569433 -13.4474406 0.000000e+00

>>>> ma1 -0.2902081 0.08598562 -3.3750776 7.379487e-04

>>>> ma2 0.8660811 0.06778412 12.7770487 0.000000e+00

>>>> mxreg1 2.5225383 0.04292728 58.7630642 0.000000e+00

>>>> mxreg2 1.6782987 0.12769640 13.1428817 0.000000e+00

>>>> omega 12.0047142 0.82992363 14.4648419 0.000000e+00

>>>> alpha1 0.0000000 0.07359329 0.0000000 1.000000e+00

>>>> shape 63.0105962 98.49368444 0.6397425 5.223400e-01

>>>>

>>>>

>>>> I can’t see any “significant” differences, can you?

>>>> It’s completely related to the optimization/starting parameters. The

>>>> “scale” is documented and not on by default (perhaps it should be).

>>>>

>>>> Alexios

>>>>

>>>>

>>>> > On Aug 19, 2018, at 9:02 PM, GALIB KHAN <

>>>>

[hidden email]> wrote:

>>>> >

>>>> > Sorry for sending this again, I didn't include r-sig-finance in the

>>>> email address. I'm still adjusting in how to respond.

>>>> >

>>>> > Alexios,

>>>> >

>>>> > Did you set the set the seed to 1, because I'm looking at your

>>>> results and the numbers do not match with the numbers that I have provided.

>>>> >

>>>> > I understand why the coefficients' estimates are similar but it

>>>> doesn't explain why other columns such as the t-value and pr are off by a

>>>> large margin. Also estimates for mu, ar*, ma*, omega, alpha1, and shape may

>>>> have large differences.

>>>> >

>>>> > Take mu as an example:

>>>> > -7.538187e+00 - (-7.877120e+00) = 0.338933, isn't that considered a

>>>> large difference to the point where it's safe to say that these two values

>>>> are not similar?

>>>> >

>>>> > Another example is the t-values for x1 and x2:

>>>> > x1 = 8.799994e+01 - 5.509361e+02 = -462.9362

>>>> > x2 = 8.508606e+01 - 5.287634e+02 = -443.6773

>>>> >

>>>> > An more alarming case that unfortunately I cannot share due to the

>>>> data being sensitive is that when the x variables' positions are switched,

>>>> the p-values are not the same. The p-value for a particular external

>>>> regressor went from 0 to 0.4385.

>>>> >

>>>> > I will attempt to re-create a separate generic dataset that is

>>>> similar to the sensitive data that I am using.

>>>> >

>>>> >

>>>> > Galib Khan

>>>> >

>>>> >

>>>> > On Sun, Aug 19, 2018 at 10:06 PM, alexios galanos <

>>>>

[hidden email]> wrote:

>>>> > I run the code you provided and obtain the following results related

>>>> to the external parameters:

>>>> >

>>>> >

>>>> > Case 1 (x1,x2)

>>>> > # x2 is second

>>>> >

>>>> > Estimate Std. Error t value Pr(>|t|)

>>>> > mxreg1 1.6724148 1.203377e-01 1.389767e+01 0.0000000

>>>> > mxreg2 2.5310286 1.878833e-02 1.347128e+02 0.0000000

>>>> >

>>>> > Case 2 (x2,x1)

>>>> > # i.e. x2 is now first

>>>> >

>>>> > mxreg1 2.5225382 0.04292725 58.7631024 0.000000e+00

>>>> > mxreg2 1.6782986 0.12769622 13.1428990 0.000000e+00

>>>> >

>>>> > Small differences in the coefficients are the result of the

>>>> optimizer. There may be an issues in the

>>>> > way starting parameters are being generated based on some recent

>>>> input from Josh Ulrich (still to investigate)

>>>> > and related to arima0 (used to generate start parameters), but

>>>> otherwise don’t see a large problem at first glance.

>>>> >

>>>> > Alexios

>>>> >

>>>> > > On Aug 19, 2018, at 5:46 PM, GALIB KHAN <

>>>>

[hidden email]> wrote:

>>>> > >

>>>> > > Recently I have discovered a problem with a package called rugarch

>>>> that

>>>> > > creates arma-garch models. The issue is that if you literally

>>>> change the

>>>> > > positions of the x variables (external regressors) then you get two

>>>> > > completely different results.

>>>> > >

>>>> > > In other words:

>>>> > >

>>>> > > - model1 = (arma(2,2) + garch(1,0) + x1 + x2)

>>>> > > - model2 = (arma(2,2) + garch(1,0) + x2 + x1)

>>>> > > - rugarch's output is essentially saying that model1 != model2

>>>> > > - When the correct result should be model1 == model2

>>>> > >

>>>> > > I may not know a lot of statistics but I know for a fact that if

>>>> you move

>>>> > > the x variables around, the output should still be the same.

>>>> > >

>>>> > > Am I wrong on this?

>>>> > >

>>>> > > Here's my stack exchange post that shows a generic R script proving

>>>> my

>>>> > > point: Should the positioning of the external regressors change the

>>>> output

>>>> > > of arma-garch? (Possible rugarch bug/error)

>>>> > > <

>>>>

https://stackoverflow.com/questions/51900177/should-the-positioning-of-the-external-regressors-change-the-output-of-arma-garc>>>> >

>>>> > >

>>>> > > Any feedback is welcomed.

>>>> > >

>>>> > > Thanks

>>>> > >

>>>> > > [[alternative HTML version deleted]]

>>>> > >

>>>> > > _______________________________________________

>>>> > >

[hidden email] mailing list

>>>> > >

https://stat.ethz.ch/mailman/listinfo/r-sig-finance>>>> > > -- Subscriber-posting only. If you want to post, subscribe first.

>>>> > > -- Also note that this is not the r-help list where general R

>>>> questions should go.

>>>> > >

>>>> >

>>>> >

>>>>

>>>>

>>>

>>

>> Galib Khan

>>

>>

>> On Sun, Aug 19, 2018 at 11:41 PM, GALIB KHAN <

>>

[hidden email]> wrote:

>>

>>> Alexios,

>>>

>>>

>>> Veryyyy interesting!!!!! No I cannot see any differences at all lol.

>>>

>>> I updated the code and indeed you are correct sir. Thank you for your

>>> time in investigating this.

>>>

>>> I will update my stack exchange post to reflect your answer in the

>>> morning.

>>>

>>> Again thank you for all your help!!!!

>>>

>>> Best,

>>> Galib Khan

>>>

>>>

>>> On Sun, Aug 19, 2018 at 11:19 PM, alexios galanos <

[hidden email]>

>>> wrote:

>>>

>>>> I did use the seed you provided.

>>>>

>>>> Use the following code for estimation:

>>>>

>>>> fit <- ugarchfit(spec = spec, data = as.matrix(temp$y),solver =

>>>> "nlminb", fit.control=list(scale=1))

>>>>

>>>> model_maker(var1)

>>>> Estimate Std. Error t value Pr(>|t|)

>>>> mu -7.3998577 0.69086641 -10.7109821 0.0000000000

>>>> ar1 0.3387323 0.08280162 4.0908900 0.0000429721

>>>> ar2 -0.8834201 0.06569477 -13.4473414 0.0000000000

>>>> ma1 -0.2902069 0.08598589 -3.3750525 0.0007380161

>>>> ma2 0.8660807 0.06778418 12.7770320 0.0000000000

>>>> mxreg1 1.6782992 0.12769644 13.1428825 0.0000000000

>>>> mxreg2 2.5225382 0.04292728 58.7630625 0.0000000000

>>>> omega 12.0047145 0.82986864 14.4658010 0.0000000000

>>>> alpha1 0.0000000 0.07358520 0.0000000 1.0000000000

>>>> shape 63.0103309 98.49188643 0.6397515 0.5223341761

>>>>

>>>> model_maker(var2)

>>>> Estimate Std. Error t value Pr(>|t|)

>>>> mu -7.3998549 0.69086651 -10.7109764 0.000000e+00

>>>> ar1 0.3387334 0.08280150 4.0909088 4.296861e-05

>>>> ar2 -0.8834206 0.06569433 -13.4474406 0.000000e+00

>>>> ma1 -0.2902081 0.08598562 -3.3750776 7.379487e-04

>>>> ma2 0.8660811 0.06778412 12.7770487 0.000000e+00

>>>> mxreg1 2.5225383 0.04292728 58.7630642 0.000000e+00

>>>> mxreg2 1.6782987 0.12769640 13.1428817 0.000000e+00

>>>> omega 12.0047142 0.82992363 14.4648419 0.000000e+00

>>>> alpha1 0.0000000 0.07359329 0.0000000 1.000000e+00

>>>> shape 63.0105962 98.49368444 0.6397425 5.223400e-01

>>>>

>>>>

>>>> I can’t see any “significant” differences, can you?

>>>> It’s completely related to the optimization/starting parameters. The

>>>> “scale” is documented and not on by default (perhaps it should be).

>>>>

>>>> Alexios

>>>>

>>>>

>>>> > On Aug 19, 2018, at 9:02 PM, GALIB KHAN <

>>>>

[hidden email]> wrote:

>>>> >

>>>> > Sorry for sending this again, I didn't include r-sig-finance in the

>>>> email address. I'm still adjusting in how to respond.

>>>> >

>>>> > Alexios,

>>>> >

>>>> > Did you set the set the seed to 1, because I'm looking at your

>>>> results and the numbers do not match with the numbers that I have provided.

>>>> >

>>>> > I understand why the coefficients' estimates are similar but it

>>>> doesn't explain why other columns such as the t-value and pr are off by a

>>>> large margin. Also estimates for mu, ar*, ma*, omega, alpha1, and shape may

>>>> have large differences.

>>>> >

>>>> > Take mu as an example:

>>>> > -7.538187e+00 - (-7.877120e+00) = 0.338933, isn't that considered a

>>>> large difference to the point where it's safe to say that these two values

>>>> are not similar?

>>>> >

>>>> > Another example is the t-values for x1 and x2:

>>>> > x1 = 8.799994e+01 - 5.509361e+02 = -462.9362

>>>> > x2 = 8.508606e+01 - 5.287634e+02 = -443.6773

>>>> >

>>>> > An more alarming case that unfortunately I cannot share due to the

>>>> data being sensitive is that when the x variables' positions are switched,

>>>> the p-values are not the same. The p-value for a particular external

>>>> regressor went from 0 to 0.4385.

>>>> >

>>>> > I will attempt to re-create a separate generic dataset that is

>>>> similar to the sensitive data that I am using.

>>>> >

>>>> >

>>>> > Galib Khan

>>>> >

>>>> >

>>>> > On Sun, Aug 19, 2018 at 10:06 PM, alexios galanos <

>>>>

[hidden email]> wrote:

>>>> > I run the code you provided and obtain the following results related

>>>> to the external parameters:

>>>> >

>>>> >

>>>> > Case 1 (x1,x2)

>>>> > # x2 is second

>>>> >

>>>> > Estimate Std. Error t value Pr(>|t|)

>>>> > mxreg1 1.6724148 1.203377e-01 1.389767e+01 0.0000000

>>>> > mxreg2 2.5310286 1.878833e-02 1.347128e+02 0.0000000

>>>> >

>>>> > Case 2 (x2,x1)

>>>> > # i.e. x2 is now first

>>>> >

>>>> > mxreg1 2.5225382 0.04292725 58.7631024 0.000000e+00

>>>> > mxreg2 1.6782986 0.12769622 13.1428990 0.000000e+00

>>>> >

>>>> > Small differences in the coefficients are the result of the

>>>> optimizer. There may be an issues in the

>>>> > way starting parameters are being generated based on some recent

>>>> input from Josh Ulrich (still to investigate)

>>>> > and related to arima0 (used to generate start parameters), but

>>>> otherwise don’t see a large problem at first glance.

>>>> >

>>>> > Alexios

>>>> >

>>>> > > On Aug 19, 2018, at 5:46 PM, GALIB KHAN <

>>>>

[hidden email]> wrote:

>>>> > >

>>>> > > Recently I have discovered a problem with a package called rugarch

>>>> that

>>>> > > creates arma-garch models. The issue is that if you literally

>>>> change the

>>>> > > positions of the x variables (external regressors) then you get two

>>>> > > completely different results.

>>>> > >

>>>> > > In other words:

>>>> > >

>>>> > > - model1 = (arma(2,2) + garch(1,0) + x1 + x2)

>>>> > > - model2 = (arma(2,2) + garch(1,0) + x2 + x1)

>>>> > > - rugarch's output is essentially saying that model1 != model2

>>>> > > - When the correct result should be model1 == model2

>>>> > >

>>>> > > I may not know a lot of statistics but I know for a fact that if

>>>> you move

>>>> > > the x variables around, the output should still be the same.

>>>> > >

>>>> > > Am I wrong on this?

>>>> > >

>>>> > > Here's my stack exchange post that shows a generic R script proving

>>>> my

>>>> > > point: Should the positioning of the external regressors change the

>>>> output

>>>> > > of arma-garch? (Possible rugarch bug/error)

>>>> > > <

>>>>

https://stackoverflow.com/questions/51900177/should-the-positioning-of-the-external-regressors-change-the-output-of-arma-garc>>>> >

>>>> > >

>>>> > > Any feedback is welcomed.

>>>> > >

>>>> > > Thanks

>>>> > >

>>>> > > [[alternative HTML version deleted]]

>>>> > >

>>>> > > _______________________________________________

>>>> > >

[hidden email] mailing list

>>>> > >

https://stat.ethz.ch/mailman/listinfo/r-sig-finance>>>> > > -- Subscriber-posting only. If you want to post, subscribe first.

>>>> > > -- Also note that this is not the r-help list where general R

>>>> questions should go.

>>>> > >

>>>> >

>>>> >

>>>>

>>>>

>>>

>>

-- Subscriber-posting only. If you want to post, subscribe first.

-- Also note that this is not the r-help list where general R questions should go.