Paper XV AcorF lag60
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 3 seconds |
| R Server | 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 9.3274 | 0 |
| 1 | 0.170672 | 1.5919 | 0.057514 |
| 2 | 0.205505 | 1.9168 | 0.029271 |
| 3 | 0.129548 | 1.2083 | 0.115095 |
| 4 | 0.104002 | 0.9701 | 0.167351 |
| 5 | 0.011953 | 0.1115 | 0.455744 |
| 6 | 0.015091 | 0.1408 | 0.444194 |
| 7 | -0.191191 | -1.7833 | 0.960988 |
| 8 | -0.09258 | -0.8635 | 0.804888 |
| 9 | -0.040012 | -0.3732 | 0.64505 |
| 10 | -0.259604 | -2.4214 | 0.991232 |
| 11 | -0.115475 | -1.0771 | 0.85779 |
| 12 | -0.207575 | -1.9361 | 0.971951 |
| 13 | -0.226032 | -2.1083 | 0.981059 |
| 14 | -0.067413 | -0.6288 | 0.734432 |
| 15 | -0.020879 | -0.1947 | 0.576976 |
| 16 | -0.179056 | -1.6701 | 0.950755 |
| 17 | 0.04668 | 0.4354 | 0.332173 |
| 18 | -0.029599 | -0.2761 | 0.608428 |
| 19 | -0.040923 | -0.3817 | 0.648196 |
| 20 | 0.091752 | 0.8558 | 0.197228 |
| 21 | -0.039098 | -0.3647 | 0.641882 |
| 22 | -0.029755 | -0.2775 | 0.608988 |
| 23 | 0.139084 | 1.2973 | 0.098981 |
| 24 | 0.044573 | 0.4158 | 0.339307 |
| 25 | 0.096578 | 0.9008 | 0.185085 |
| 26 | 0.173101 | 1.6146 | 0.055012 |
| 27 | 0.056943 | 0.5311 | 0.298342 |
| 28 | 0.076103 | 0.7098 | 0.239849 |
| 29 | 0.131713 | 1.2285 | 0.111279 |
| 30 | -0.077508 | -0.7229 | 0.764173 |
| 31 | 0.008326 | 0.0777 | 0.46914 |
| 32 | -0.06496 | -0.6059 | 0.726924 |
| 33 | -0.075723 | -0.7063 | 0.759054 |
| 34 | -0.026274 | -0.2451 | 0.59651 |
| 35 | -0.025468 | -0.2376 | 0.593607 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.170672 | 1.5919 | 0.057514 |
| 1 | 0.181668 | 1.6945 | 0.046875 |
| 2 | 0.07439 | 0.6939 | 0.244807 |
| 3 | 0.042591 | 0.3973 | 0.346072 |
| 4 | -0.046714 | -0.4357 | 0.667939 |
| 5 | -0.016869 | -0.1573 | 0.562332 |
| 6 | -0.212731 | -1.9842 | 0.974809 |
| 7 | -0.05017 | -0.468 | 0.679507 |
| 8 | 0.053775 | 0.5016 | 0.308615 |
| 9 | -0.220553 | -2.0572 | 0.978668 |
| 10 | -0.017717 | -0.1653 | 0.565435 |
| 11 | -0.124969 | -1.1656 | 0.876526 |
| 12 | -0.152383 | -1.4213 | 0.920603 |
| 13 | 0.040482 | 0.3776 | 0.353328 |
| 14 | 0.050003 | 0.4664 | 0.321049 |
| 15 | -0.144811 | -1.3507 | 0.909855 |
| 16 | 0.031037 | 0.2895 | 0.386446 |
| 17 | -0.043295 | -0.4038 | 0.656334 |
| 18 | -0.10298 | -0.9605 | 0.830275 |
| 19 | 0.022948 | 0.214 | 0.415506 |
| 20 | -0.088567 | -0.8261 | 0.794497 |
| 21 | -0.090167 | -0.841 | 0.798678 |
| 22 | 0.051212 | 0.4777 | 0.317041 |
| 23 | -0.008957 | -0.0835 | 0.533194 |
| 24 | 0.049175 | 0.4587 | 0.323806 |
| 25 | 0.055909 | 0.5215 | 0.301676 |
| 26 | 0.024398 | 0.2276 | 0.410258 |
| 27 | -0.033049 | -0.3083 | 0.620689 |
| 28 | 0.012643 | 0.1179 | 0.453198 |
| 29 | -0.092744 | -0.8651 | 0.805308 |
| 30 | -0.035785 | -0.3338 | 0.630327 |
| 31 | -0.111362 | -1.0387 | 0.849093 |
| 32 | 0.015767 | 0.1471 | 0.441711 |
| 33 | -0.003155 | -0.0294 | 0.511703 |
| 34 | -0.000271 | -0.0025 | 0.501006 |
| 35 | -0.120044 | -1.1197 | 0.867038 |
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par1 = 10*log10(length(x))
} else {
par1 <- as.numeric(par1)
}
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
if (par2 == 0) {
x <- log(x)
} else {
x <- (x ^ par2 - 1) / par2
}
if (par3 > 0) x <- diff(x,lag=1,difference=par3)
if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
bitmap(file='pic1.png')
racf <- acf(x,par1,main='Autocorrelation',xlab='lags',ylab='ACF')
dev.off()
bitmap(file='pic2.png')
rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF')
dev.off()
(myacf <- c(racf$acf))
(mypacf <- c(rpacf$acf))
lengthx <- length(x)
sqrtn <- sqrt(lengthx)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:par1) {
a<-table.row.start(a)
a<-table.element(a,i-1,header=TRUE)
a<-table.element(a,round(myacf[i],6))
mytstat <- myacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(mytstat,lengthx),6))
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,'Partial Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:par1) {
a<-table.row.start(a)
a<-table.element(a,i-1,header=TRUE)
a<-table.element(a,round(mypacf[i],6))
mytstat <- mypacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(mytstat,lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
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