Autocorrelatie - Investeringsgoederen
| 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 | 6.9282 | 0 |
| 1 | 0.005795 | 0.0402 | 0.48407 |
| 2 | -0.119795 | -0.83 | 0.794667 |
| 3 | 0.202047 | 1.3998 | 0.083998 |
| 4 | 0.106968 | 0.7411 | 0.231123 |
| 5 | 0.038945 | 0.2698 | 0.394229 |
| 6 | 0.151448 | 1.0493 | 0.149656 |
| 7 | -0.029756 | -0.2062 | 0.581229 |
| 8 | 0.032416 | 0.2246 | 0.411628 |
| 9 | 0.098071 | 0.6795 | 0.250056 |
| 10 | 0.022407 | 0.1552 | 0.438641 |
| 11 | 0.007036 | 0.0487 | 0.480661 |
| 12 | -0.262112 | -1.816 | 0.962188 |
| 13 | -0.080472 | -0.5575 | 0.710119 |
| 14 | 0.016763 | 0.1161 | 0.454014 |
| 15 | -0.017979 | -0.1246 | 0.549306 |
| 16 | -0.058715 | -0.4068 | 0.657015 |
| 17 | 0.047639 | 0.3301 | 0.371398 |
| 18 | -0.057323 | -0.3971 | 0.653489 |
| 19 | 0.004135 | 0.0286 | 0.488632 |
| 20 | 0.042528 | 0.2946 | 0.384769 |
| 21 | 0.047251 | 0.3274 | 0.372407 |
| 22 | -0.155396 | -1.0766 | 0.856484 |
| 23 | 0.086697 | 0.6007 | 0.275448 |
| 24 | 0.076827 | 0.5323 | 0.298497 |
| 25 | 0.009901 | 0.0686 | 0.472798 |
| 26 | 0.16959 | 1.175 | 0.122905 |
| 27 | -0.014561 | -0.1009 | 0.539967 |
| 28 | -0.09847 | -0.6822 | 0.750811 |
| 29 | 0.041056 | 0.2844 | 0.388647 |
| 30 | 0.086602 | 0.6 | 0.275665 |
| 31 | -0.090729 | -0.6286 | 0.733701 |
| 32 | -0.083037 | -0.5753 | 0.716112 |
| 33 | -0.065034 | -0.4506 | 0.672835 |
| 34 | 0.135442 | 0.9384 | 0.176376 |
| 35 | -0.035646 | -0.247 | 0.597006 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.005795 | 0.0402 | 0.48407 |
| 1 | -0.119833 | -0.8302 | 0.79474 |
| 2 | 0.206491 | 1.4306 | 0.079509 |
| 3 | 0.090023 | 0.6237 | 0.267888 |
| 4 | 0.089482 | 0.6199 | 0.269112 |
| 5 | 0.142171 | 0.985 | 0.164784 |
| 6 | -0.059864 | -0.4148 | 0.659915 |
| 7 | 0.037571 | 0.2603 | 0.397873 |
| 8 | 0.017916 | 0.1241 | 0.450866 |
| 9 | 0.014446 | 0.1001 | 0.460347 |
| 10 | 0.002668 | 0.0185 | 0.492664 |
| 11 | -0.335867 | -2.327 | 0.987883 |
| 12 | -0.109932 | -0.7616 | 0.774998 |
| 13 | -0.113381 | -0.7855 | 0.781997 |
| 14 | 0.052697 | 0.3651 | 0.358323 |
| 15 | 0.047439 | 0.3287 | 0.371919 |
| 16 | 0.139346 | 0.9654 | 0.169588 |
| 17 | 0.068088 | 0.4717 | 0.319629 |
| 18 | 0.060464 | 0.4189 | 0.338577 |
| 19 | 0.075348 | 0.522 | 0.302027 |
| 20 | 0.136865 | 0.9482 | 0.173882 |
| 21 | -0.153047 | -1.0603 | 0.852852 |
| 22 | 0.07099 | 0.4918 | 0.31254 |
| 23 | -0.153995 | -1.0669 | 0.854325 |
| 24 | -0.036215 | -0.2509 | 0.598521 |
| 25 | 0.113209 | 0.7843 | 0.218349 |
| 26 | -0.061314 | -0.4248 | 0.663558 |
| 27 | -0.034773 | -0.2409 | 0.594676 |
| 28 | -0.056368 | -0.3905 | 0.651063 |
| 29 | 0.065392 | 0.4531 | 0.326277 |
| 30 | -0.019112 | -0.1324 | 0.552395 |
| 31 | -0.116769 | -0.809 | 0.788746 |
| 32 | -0.017008 | -0.1178 | 0.546656 |
| 33 | 0.044376 | 0.3074 | 0.379917 |
| 34 | 0.006576 | 0.0456 | 0.481925 |
| 35 | -0.092478 | -0.6407 | 0.73762 |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/30/t1196437935mirh28n6qznqzct/2v6z71196438508.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/30/t1196437935mirh28n6qznqzct/2v6z71196438508.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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Software written by Ed van Stee & Patrick Wessa
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