ACP/PACF Serie 2 - Consumptieprijsindex
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 2 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 | 7.6811 | 0 |
| 1 | -0.069861 | -0.5366 | 0.703223 |
| 2 | -0.179963 | -1.3823 | 0.913958 |
| 3 | -0.159994 | -1.2289 | 0.888013 |
| 4 | -0.152015 | -1.1677 | 0.876178 |
| 5 | 0.215753 | 1.6572 | 0.051391 |
| 6 | 0.073681 | 0.566 | 0.286786 |
| 7 | -0.203211 | -1.5609 | 0.938051 |
| 8 | 0.029794 | 0.2288 | 0.409889 |
| 9 | 0.171142 | 1.3146 | 0.096872 |
| 10 | 0.013447 | 0.1033 | 0.459041 |
| 11 | 0.226019 | 1.7361 | 0.043884 |
| 12 | -0.448134 | -3.4422 | 0.999467 |
| 13 | 0.01849 | 0.142 | 0.443773 |
| 14 | 0.152578 | 1.172 | 0.122959 |
| 15 | -0.018075 | -0.1388 | 0.554973 |
| 16 | 0.027622 | 0.2122 | 0.416352 |
| 17 | -0.110283 | -0.8471 | 0.799818 |
| 18 | -0.151342 | -1.1625 | 0.87514 |
| 19 | 0.10174 | 0.7815 | 0.218822 |
| 20 | 0.163287 | 1.2542 | 0.107352 |
| 21 | -0.050562 | -0.3884 | 0.650433 |
| 22 | 0.142511 | 1.0947 | 0.139059 |
| 23 | -0.175759 | -1.35 | 0.908918 |
| 24 | -0.069006 | -0.53 | 0.700965 |
| 25 | -0.045831 | -0.352 | 0.636966 |
| 26 | -0.065521 | -0.5033 | 0.691677 |
| 27 | 0.096489 | 0.7411 | 0.230772 |
| 28 | 0.078233 | 0.6009 | 0.275098 |
| 29 | -0.039557 | -0.3038 | 0.618841 |
| 30 | -0.018349 | -0.1409 | 0.555801 |
| 31 | 0.051389 | 0.3947 | 0.347234 |
| 32 | -0.126651 | -0.9728 | 0.832694 |
| 33 | 0.073479 | 0.5644 | 0.28731 |
| 34 | -0.167436 | -1.2861 | 0.898284 |
| 35 | -0.036924 | -0.2836 | 0.611153 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | -0.069861 | -0.5366 | 0.703223 |
| 1 | -0.18575 | -1.4268 | 0.920542 |
| 2 | -0.195547 | -1.502 | 0.930788 |
| 3 | -0.239257 | -1.8378 | 0.964433 |
| 4 | 0.107271 | 0.824 | 0.206639 |
| 5 | 0.006554 | 0.0503 | 0.48001 |
| 6 | -0.219925 | -1.6893 | 0.951778 |
| 7 | 0.022699 | 0.1744 | 0.431093 |
| 8 | 0.216304 | 1.6615 | 0.050963 |
| 9 | -0.014489 | -0.1113 | 0.544117 |
| 10 | 0.268533 | 2.0626 | 0.021779 |
| 11 | -0.328308 | -2.5218 | 0.992802 |
| 12 | 0.165961 | 1.2748 | 0.103693 |
| 13 | 0.009108 | 0.07 | 0.47223 |
| 14 | -0.045444 | -0.3491 | 0.635858 |
| 15 | -0.109045 | -0.8376 | 0.79718 |
| 16 | 0.002813 | 0.0216 | 0.491418 |
| 17 | -0.178676 | -1.3724 | 0.912438 |
| 18 | -0.120164 | -0.923 | 0.820117 |
| 19 | 0.076793 | 0.5899 | 0.27877 |
| 20 | 0.089514 | 0.6876 | 0.247209 |
| 21 | 0.101202 | 0.7773 | 0.220029 |
| 22 | 0.103953 | 0.7985 | 0.213897 |
| 23 | -0.229894 | -1.7659 | 0.958703 |
| 24 | -0.05911 | -0.454 | 0.674265 |
| 25 | -0.027018 | -0.2075 | 0.581845 |
| 26 | 0.014386 | 0.1105 | 0.456193 |
| 27 | -0.009429 | -0.0724 | 0.528746 |
| 28 | -0.012058 | -0.0926 | 0.536741 |
| 29 | -0.157505 | -1.2098 | 0.884414 |
| 30 | -0.001363 | -0.0105 | 0.504158 |
| 31 | -0.067083 | -0.5153 | 0.695855 |
| 32 | 0.041219 | 0.3166 | 0.376328 |
| 33 | -0.122898 | -0.944 | 0.825489 |
| 34 | -0.00445 | -0.0342 | 0.513576 |
| 35 | -0.052395 | -0.4025 | 0.655598 |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/06/t1196945877m4g6u840ef0h9z7/1v3291196946625.png (open in new window) |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/06/t1196945877m4g6u840ef0h9z7/2bses1196946625.ps (open in new window) |
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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