[R] Problems with arima0
Pascal Grandeau
pgrandeau at free.fr
Fri Dec 28 13:58:06 CET 2001
I use R 1.3.1 and now R 1.4.0 under Windows 98 and 2000 and I have some
problems with arima0.
I made an example : it is just for test (it is zipped in the attached file
modelfr2.zip. The file is in the french csv format with sep= ";" and
dec=".", so I use read.csv2).
I want to do a regression with ARMA (p=1; q=3) errors of the first column
(sendout) on the other (the second column which is the constant is useless),
so this is what I do with R :
> dd<-read.csv2("c:/modelfr2.csv")
> names(dd) # Just to verify !
[1] "sendout" "cste" "TempLT17" "lag2"
[5] "We" "hivergaz" "lag7" "octobre"
[9] "vendredi" "Tmoycarre" "Tmaxi" "avril"
[13] "TempGT17" "vacances" "Lineartrend" "TempLT171"
[17] "feriesemaine" "vacoct" "MoistransT17" "jeudi"
[21] "mercredi" "Tmoycarre1" "WET17" "vacdec"
[25] "MoistransTnx" "septembre" "MoistransDeltaT" "MoistransDTmax"
[29] "Rechauffement" "samedi" "janvier" "automne"
[33] "periode" "quadratrend" "aout" "tempeff"
[37] "MoistransDTmin" "MoistransTmin" "hiver" "juin"
> ari<-arima0(dd[,1],xreg=as.matrix(dd[,-1:-2]),order=c(1,0,3))
> ari$coef
ar1 ma1 ma2 ma3
intercept
2.201316e-01 3.206251e-01 3.500737e-02
2.473141e-02 -5.018293e+06
TempLT17 lag2 We hivergaz
lag7
1.028222e+06 2.917059e-01 -1.566802e+06 2.566452e+06
9.574060e-02
octobre vendredi Tmoycarre Tmaxi
avril
-2.295546e+06 -8.999837e+05
2.736549e+04 -1.100721e+05 -2.612360e+06
TempGT17 vacances Lineartrend TempLT171
feriesemaine
-1.010776e+06 -1.454308e+05 -1.081656e+07
2.542789e+05 -9.502527e+05
vacoct MoistransT17 jeudi mercredi
Tmoycarre1
4.843674e+05
3.627698e+05 -5.517601e+05 -4.267915e+05 -6.751992e+03
WET17 vacdec MoistransTnx septembre
MoistransDeltaT
-3.878577e+04 -5.872619e+05 6.298442e+03 -3.339184e+06
3.031612e+05
MoistransDTmax Rechauffement samedi janvier
automne
-1.019058e+05 1.479148e+05 -2.047690e+05 -1.078147e+06
4.288875e+05
periode quadratrend aout tempeff
MoistransDTmin
-3.476783e+04 1.109006e+07 -2.105259e+05
3.437851e+05 -6.411536e+04
MoistransTmin hiver juin
1.043679e+05 -2.434158e+05 1.215601e+05
> ari$code
[1] 0
> diag(ari$var.coef)
ar1 ma1 ma2 ma3
intercept
3.132705e-01 3.117503e-01 8.869646e-02
6.965824e-03 -1.046849e+06
TempLT17 lag2 We hivergaz
lag7
1.171641e+07 9.928982e-05 1.214256e+07 3.898401e+06
9.109221e-05
octobre vendredi Tmoycarre Tmaxi
avril
3.532436e+07 3.095100e+06 1.722773e+05 7.896726e+05
2.289079e+07
TempGT17 vacances Lineartrend TempLT171
feriesemaine
5.674179e+06 -5.124374e+05 9.341290e+05
4.346287e+06 -4.689245e+06
vacoct MoistransT17 jeudi mercredi
Tmoycarre1
5.112234e+05 6.510381e+05 -6.286289e+05 2.762435e+06
2.200297e+05
WET17 vacdec MoistransTnx septembre
MoistransDeltaT
1.511598e+06 1.423986e+06 1.284414e+05 3.616154e+07
6.749349e+05
MoistransDTmax Rechauffement samedi janvier
automne
3.733944e+06 4.233166e+06 -1.137565e+06 -1.455328e+06
4.870733e+06
periode quadratrend aout tempeff
MoistransDTmin
1.214202e+05 -6.837660e+05 -1.292829e+06 1.243424e+07
2.729197e+07
MoistransTmin hiver juin
5.775706e+06 6.265115e+06 -9.041021e+06
> ari<-arima0(dd[,1],xreg=as.matrix(dd[,-1:-2]),order=c(1,0,3),delta=-1)
> ari$coef
ar1 ma1 ma2 ma3
intercept
2.209774e-01 3.197254e-01 3.456447e-02
2.639046e-02 -5.018293e+06
TempLT17 lag2 We hivergaz
lag7
1.028222e+06 2.916751e-01 -1.566802e+06 2.566452e+06
9.576419e-02
octobre vendredi Tmoycarre Tmaxi
avril
-2.295546e+06 -8.999837e+05
2.736549e+04 -1.100721e+05 -2.612360e+06
TempGT17 vacances Lineartrend TempLT171
feriesemaine
-1.010776e+06 -1.454308e+05 -1.081656e+07
2.542789e+05 -9.502527e+05
vacoct MoistransT17 jeudi mercredi
Tmoycarre1
4.843674e+05
3.627698e+05 -5.517601e+05 -4.267915e+05 -6.751992e+03
WET17 vacdec MoistransTnx septembre
MoistransDeltaT
-3.878577e+04 -5.872619e+05 6.298442e+03 -3.339184e+06
3.031612e+05
MoistransDTmax Rechauffement samedi janvier
automne
-1.019058e+05 1.479148e+05 -2.047690e+05 -1.078147e+06
4.288875e+05
periode quadratrend aout tempeff
MoistransDTmin
-3.476783e+04 1.109006e+07 -2.105259e+05
3.437851e+05 -6.411536e+04
MoistransTmin hiver juin
1.043679e+05 -2.434158e+05 1.215601e+05
> ari$code
[1] 0
> diag(ari$var.coef)
ar1 ma1 ma2 ma3
intercept
2.984138e-01 2.971397e-01 8.488563e-02 6.717204e-03
1.976934e+06
TempLT17 lag2 We hivergaz
lag7
7.650781e+05 9.506931e-05 2.345793e+06 9.386527e+05
8.919501e-05
octobre vendredi Tmoycarre Tmaxi
avril
-7.568479e+05 3.492818e+05 1.299257e+05 -2.127121e+05
3.733916e+06
TempGT17 vacances Lineartrend TempLT171
feriesemaine
-9.460373e+05 1.194900e+06 4.093315e+05 9.296041e+05
2.080155e+06
vacoct MoistransT17 jeudi mercredi
Tmoycarre1
2.358321e+06 1.698256e+06 9.177695e+05 1.282622e+06
1.686617e+05
WET17 vacdec MoistransTnx septembre
MoistransDeltaT
-4.289835e+04 2.335913e+06 1.251494e+05
1.733809e+06 -9.864234e+05
MoistransDTmax Rechauffement samedi janvier
automne
1.854713e+06 2.666766e+05 4.113436e+04 4.021349e+05
6.950620e+04
periode quadratrend aout tempeff
MoistransDTmin
1.000572e+05 1.266920e+06 1.743747e+04 1.754632e+06
2.770144e+06
MoistransTmin hiver juin
6.929513e+05 6.794158e+05 2.348674e+05
>
So, I get negatives values for diag(ari$var.coef) but the convergence seems
good : ari$code=0 !
I try the same thing with SAS, with the procedure :
PROC ARIMA DATA=WORK._egtemp_;
IDENTIFY
VAR=sendout crosscor=(TempLT17 lag2 We hivergaz lag7 octobre vendredi
Tmoycarre Tmaxi avril TempGT17 vacances Lineartrend TempLT171 feriesemaine
vacoct MoistransT17 jeudi mercredi Tmoycarre1 WET17 vacdec MoistransTnx
septembre MoistransDeltaT MoistransDTmax Rechauffement samedi janvier
automne periode quadratrend aout tempeff MoistransDTmin MoistransTmin hiver
juin)
;
ESTIMATE
METHOD=ML
MAXITER=150
P=(1)
Q=(1,2,3)
input=(TempLT17 lag2 We hivergaz lag7 octobre vendredi Tmoycarre Tmaxi
avril TempGT17 vacances Lineartrend TempLT171 feriesemaine vacoct
MoistransT17 jeudi mercredi Tmoycarre1 WET17 vacdec MoistransTnx septembre
MoistransDeltaT MoistransDTmax Rechauffement samedi janvier automne periode
quadratrend aout tempeff MoistransDTmin MoistransTmin hiver juin)
;
FORECAST
ID=Date
INTERVAL = DAY
;
RUN;
and here are the results :
Maximum Likelihood Estimation
Parameter Estimate Standard Error t Value Approx Lag Variable Shift
Pr>|t|
MU 5677704.9 4178966.3 1.36 0.1743 0 sendout 0
MA1,1 0.13339 0.04068 3.28 0.0010 1 sendout 0
MA1,2 0.31361 0.04518 6.94 <.0001 2 sendout 0
MA1,3 0.12087 0.03985 3.03 0.0024 3 sendout 0
AR1,1 0.98183 0.0090420 108.59 <.0001 1 sendout 0
Intercept 914801.6 31603.0 28.95 <.0001 0 TempLT17 0
NUM2 0.08117 0.02464 3.29 0.0010 0 lag2 0
NUM3 -1260988.9 82649.5 -15.26 <.0001 0 We 0
NUM4 1883728.1 444508.7 4.24 <.0001 0 hivergaz 0
NUM5 0.04916 0.01524 3.23 0.0013 0 lag7 0
NUM6 -1291028.7 678103.9 -1.90 0.0569 0 octobre 0
NUM7 -537376.7 86014.1 -6.25 <.0001 0 vendredi 0
NUM8 20164.3 1676.0 12.03 <.0001 0 Tmoycarre 0
NUM9 -72794.9 14816.2 -4.91 <.0001 0 Tmaxi 0
NUM10 -1735412.6 550555.1 -3.15 0.0016 0 avril 0
NUM11 -777787.6 74365.4 -10.46 <.0001 0 TempGT17 0
NUM12 -176589.8 146977.0 -1.20 0.2296 0 vacances 0
NUM13 -21270415 5072492.7 -4.19 <.0001 0 Lineartrend 0
NUM14 144286.3 29856.4 4.83 <.0001 0 TempLT171 0
NUM15 -746670.3 101013.5 -7.39 <.0001 0 feriesemaine 0
NUM16 143734.4 291312.3 0.49 0.6217 0 vacoct 0
NUM17 197695.0 45790.1 4.32 <.0001 0 MoistransT17 0
NUM18 -180919.2 83234.9 -2.17 0.0297 0 jeudi 0
NUM19 -104867.0 67638.4 -1.55 0.1210 0 mercredi 0
NUM20 2414.5 1067.0 2.26 0.0236 0 Tmoycarre1 0
NUM21 -38796.3 8825.9 -4.40 <.0001 0 WET17 0
NUM22 -890757.8 322913.6 -2.76 0.0058 0 vacdec 0
NUM23 2390.4 1859.2 1.29 0.1985 0 MoistransTnx 0
NUM24 -1688542.1 680933.2 -2.48 0.0131 0 septembre 0
NUM25 193932.8 41955.6 4.62 <.0001 0 MoistransDeltaT 0
NUM26 -59055.8 18549.9 -3.18 0.0015 0 MoistransDTmax 0
NUM27 108044.1 43874.8 2.46 0.0138 0 Rechauffement 0
NUM28 -163420.7 54042.0 -3.02 0.0025 0 samedi 0
NUM29 -231656.4 350368.4 -0.66 0.5085 0 janvier 0
NUM30 662353.6 285263.7 2.32 0.0202 0 automne 0
NUM31 -59281.8 14180.1 -4.18 <.0001 0 periode 0
NUM32 21225993 5095024.2 4.17 <.0001 0 quadratrend 0
NUM33 -120509.9 315387.3 -0.38 0.7024 0 aout 0
NUM34 -65665.9 54566.7 -1.20 0.2288 0 tempeff 0
NUM35 -26494.1 17907.6 -1.48 0.1390 0 MoistransDTmin 0
NUM36 45824.8 46569.2 0.98 0.3251 0 MoistransTmin 0
NUM37 480699.1 289296.2 1.66 0.0966 0 hiver 0
NUM38 210529.4 248285.9 0.85 0.3965 0 juin 0
and the results seems good but are very differents.
I try then another tool which is Matrixer and I got almost the same results
than with SAS.
Finally, with S-Plus 4.5 and arima.mle, I got also almost the same results
than with SAS.
Certainly I did not understood the use of arima0 because something seems to
go wrong.
Another problem is if I put transform.pars=1 then R crash under Windows :
error in ts.dll.
Please can you explain me how I can make a regression with ARMA errors with
R and get
the coefficientd and the estimations of standard errors for the
coefficients.
Maybe I can do this with dse but I did not well understood how to define the
problem with dse.
Thank you very much.
Pascal Grandeau
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