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arima backward selection: dow jones

*Unverified author*
R Software Module: /rwasp_arimabackwardselection.wasp (opens new window with default values)
Title produced by software: ARIMA Backward Selection
Date of computation: Wed, 30 Dec 2009 06:24:50 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd.htm/, Retrieved Wed, 30 Dec 2009 14:27:44 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd.htm/},
    year = {2009},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2009},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
10967,87 10433,56 10665,78 10666,71 10682,74 10777,22 10052,6 10213,97 10546,82 10767,2 10444,5 10314,68 9042,56 9220,75 9721,84 9978,53 9923,81 9892,56 10500,98 10179,35 10080,48 9492,44 8616,49 8685,4 8160,67 8048,1 8641,21 8526,63 8474,21 7916,13 7977,64 8334,59 8623,36 9098,03 9154,34 9284,73 9492,49 9682,35 9762,12 10124,63 10540,05 10601,61 10323,73 10418,4 10092,96 10364,91 10152,09 10032,8 10204,59 10001,6 10411,75 10673,38 10539,51 10723,78 10682,06 10283,19 10377,18 10486,64 10545,38 10554,27 10532,54 10324,31 10695,25 10827,81 10872,48 10971,19 11145,65 11234,68 11333,88 10997,97 11036,89 11257,35 11533,59 11963,12 12185,15 12377,62 12512,89 12631,48 12268,53 12754,8 13407,75 13480,21 13673,28 13239,71 13557,69 13901,28 13200,58 13406,97 12538,12 12419,57 12193,88 12656,63 12812,48 12056,67 11322,38 11530,75 11114,08 9181,73 8614,55 8595,56 8396,20 7690,50 7235,47 7992,12 8398,37 8593 8679,75 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.6955-0.15510.1324-0.5013-0.1461-0.0930.1707
(p-val)(0.036 )(0.2384 )(0.1741 )(0.1187 )(0.8175 )(0.5114 )(0.7872 )
Estimates ( 2 )0.6953-0.15480.1337-0.50150-0.09250.0256
(p-val)(0.0339 )(0.2381 )(0.1691 )(0.1138 )(NA )(0.5074 )(0.8135 )
Estimates ( 3 )0.6923-0.15620.1348-0.50160-0.09230
(p-val)(0.0365 )(0.2326 )(0.1646 )(0.1181 )(NA )(0.5083 )(NA )
Estimates ( 4 )0.6762-0.14130.1394-0.4936000
(p-val)(0.0333 )(0.263 )(0.1514 )(0.1092 )(NA )(NA )(NA )
Estimates ( 5 )0.396700.0977-0.2436000
(p-val)(0.5481 )(NA )(0.2854 )(0.7504 )(NA )(NA )(NA )
Estimates ( 6 )0.182800.09450000
(p-val)(0.054 )(NA )(0.3145 )(NA )(NA )(NA )(NA )
Estimates ( 7 )0.1833000000
(p-val)(0.0547 )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )0000000
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )


Estimated ARIMA Residuals
Value
10.9678643254657
-525.261861701565
330.145024194708
-41.6298418909046
15.8595553657815
91.5421209822089
-741.935708646335
294.173861127316
303.275106855848
159.377315591308
-363.089880096104
-70.6775446637057
-1248.32739525331
411.336266756758
468.432441535871
164.853331439532
-101.764551782688
-21.2212576510628
614.147306257388
-433.137445539844
-39.9236476299611
-569.919719370624
-768.177674508976
229.448685317095
-537.359397574292
-16.4007388019418
613.741131692614
-223.281523658225
-31.4204879689114
-548.472787391605
163.791442435942
345.676828547458
223.350417005609
421.746024705728
-30.6845747582229
120.069852308687
183.862929187173
151.783035262881
44.9736362871135
347.890248955138
348.981414676295
-14.5756020942135
-289.162335142555
145.598123609698
-342.790530668382
331.594625548942
-262.661309974295
-80.2856698336855
193.652731630204
-234.474606142614
447.352748709991
186.46025083303
-181.819924355854
208.804863637650
-75.4918631695673
-391.223817054135
167.092420700301
92.2340955157779
38.6788498261212
-1.87550302588534
-23.3593040841024
-204.247460320865
409.103103423231
64.5764165403016
20.3752250406596
90.52315934344
156.369043178662
57.0560528107544
82.8831335649502
-354.090760983452
100.483502237417
213.326983694798
235.835457999878
378.902445422691
143.308404584448
151.777718133508
99.9952916684942
93.7985530420192
-384.684439970037
552.789225795808
563.829449159035
-47.2086278643728
179.789980434871
-468.954672611648
397.442021568508
285.312597000822
-763.671045023233
334.809951825660
-906.675879630793
40.6874413354144
-203.962890981973
504.113063975359
71.0400489405965
-784.373221766846
-595.769829043311
342.946118775604
-454.858761755264
-1855.98530565549
-213.030872113150
84.9592340180934
-195.879630533509
-669.162535184868
-325.693679173156
840.045077321579
267.575879051113
120.175018653952
51.0794202599845
678.980997829489
132.986701691718
174.656498846451
340.735405041427
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd/12ija1262179480.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd/12ija1262179480.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd/2j1ej1262179480.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd/2j1ej1262179480.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd/325kf1262179480.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd/325kf1262179480.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd/4xr8t1262179480.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd/4xr8t1262179480.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd/5votn1262179480.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd/5votn1262179480.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd/6fevn1262179480.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd/6fevn1262179480.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd/7lys61262179480.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/30/t1262179656gl9a454hvsugegd/7lys61262179480.ps (open in new window)


 
Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
 
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
 
R code (references can be found in the software module):
library(lattice)
if (par1 == 'TRUE') par1 <- TRUE
if (par1 == 'FALSE') par1 <- FALSE
par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
armaGR <- function(arima.out, names, n){
try1 <- arima.out$coef
try2 <- sqrt(diag(arima.out$var.coef))
try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
try.data.frame[,1] <- try1
for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
vector <- rep(NA,length(names))
vector[is.na(try.data.frame[,4])] <- 0
maxi <- which.max(try.data.frame[,4])
continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
vector[maxi] <- 0
list(summary=try.data.frame,next.vector=vector,continue=continue)
}
arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
mylist <- rep(list(NULL), nrc)
names <- NULL
if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
mylist[[1]] <- arima.out
last.arma <- armaGR(arima.out, names, length(series))
mystop <- FALSE
i <- 1
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- 2
aic <- arima.out$aic
while(!mystop){
mylist[[i]] <- arima.out
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
aic <- c(aic, arima.out$aic)
last.arma <- armaGR(arima.out, names, length(series))
mystop <- !last.arma$continue
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- i+1
}
list(coeff, pval, mylist, aic=aic)
}
arimaSelectplot <- function(arimaSelect.out,noms,choix){
noms <- names(arimaSelect.out[[3]][[1]]$coef)
coeff <- arimaSelect.out[[1]]
k <- min(which(is.na(coeff[,1])))-1
coeff <- coeff[1:k,]
pval <- arimaSelect.out[[2]][1:k,]
aic <- arimaSelect.out$aic[1:k]
coeff[coeff==0] <- NA
n <- ncol(coeff)
if(missing(choix)) choix <- k
layout(matrix(c(1,1,1,2,
3,3,3,2,
3,3,3,4,
5,6,7,7),nr=4),
widths=c(10,35,45,15),
heights=c(30,30,15,15))
couleurs <- rainbow(75)[1:50]#(50)
ticks <- pretty(coeff)
par(mar=c(1,1,3,1))
plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
title('aic',line=2)
par(mar=c(3,0,0,0))
plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
ytop = rep(1,50),
ybottom= rep(0,50),col=couleurs,border=NA)
axis(1,ticks)
rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
par(mar=c(1,1,3,1))
image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
if(pval[j,i]<.01) symb = 'green'
else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red'
else symb = 'black'
polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5),
c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
col=symb)
if(j==choix) {
rect(xleft=i-.5,
xright=i+.5,
ybottom=k-j+1.5,
ytop=k-j+.5,
lwd=4)
text(i,
k-j+1,
round(coeff[j,i],2),
cex=1.2,
font=2)
}
else{
rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
}
}
axis(3,1:n,noms)
par(mar=c(0.5,0,0,0.5))
plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
cols <- c('green','orange','red','black')
niv <- c('0','0.01','0.05','0.1')
for(i in 0:3){
polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i),
c(.4 ,.7 , .4 , .4),
col=cols[i+1])
text(2*i,0.5,niv[i+1],cex=1.5)
}
text(8,.5,1,cex=1.5)
text(4,0,'p-value',cex=2)
box()
residus <- arimaSelect.out[[3]][[choix]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
}
a<-table.row.end(a)
for (j in 1:nrows) {
a<-table.row.start(a)
mydum <- 'Estimates ('
mydum <- paste(mydum,j)
mydum <- paste(mydum,')')
a<-table.element(a,mydum, header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,round(selection[[1]][j,i],4))
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(p-val)', header=TRUE)
for (i in 1:ncols) {
mydum <- '('
mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
mydum <- paste(mydum,')')
a<-table.element(a,mydum)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Value', 1,TRUE)
a<-table.row.end(a)
for (i in (par4*par5+par3):length(resid)) {
a<-table.row.start(a)
a<-table.element(a,resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
 





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