WS 7 Q2 G6 autocor.
| 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 | 7.746 | 0 |
| 1 | 0.096846 | 0.7502 | 0.228042 |
| 2 | -0.101942 | -0.7896 | 0.783576 |
| 3 | 0.202714 | 1.5702 | 0.060812 |
| 4 | 0.010589 | 0.082 | 0.46745 |
| 5 | 0.09777 | 0.7573 | 0.22591 |
| 6 | 0.1958 | 1.5167 | 0.067302 |
| 7 | 0.0617 | 0.4779 | 0.317219 |
| 8 | 0.054456 | 0.4218 | 0.337335 |
| 9 | 0.126079 | 0.9766 | 0.166344 |
| 10 | -0.271355 | -2.1019 | 0.980116 |
| 11 | -0.041587 | -0.3221 | 0.625762 |
| 12 | 0.496899 | 3.849 | 0.000145 |
| 13 | -0.068817 | -0.5331 | 0.702017 |
| 14 | -0.219389 | -1.6994 | 0.952787 |
| 15 | 0.064602 | 0.5004 | 0.309311 |
| 16 | -0.09502 | -0.736 | 0.767707 |
| 17 | -0.021127 | -0.1637 | 0.564722 |
| 18 | 0.022245 | 0.1723 | 0.431886 |
| 19 | -0.029493 | -0.2284 | 0.589963 |
| 20 | -0.06128 | -0.4747 | 0.681627 |
| 21 | 0.039106 | 0.3029 | 0.381502 |
| 22 | -0.284982 | -2.2075 | 0.984441 |
| 23 | 0.001413 | 0.0109 | 0.49565 |
| 24 | 0.37512 | 2.9057 | 0.002563 |
| 25 | -0.062297 | -0.4826 | 0.684415 |
| 26 | -0.123103 | -0.9536 | 0.827933 |
| 27 | 0.015068 | 0.1167 | 0.453736 |
| 28 | -0.097966 | -0.7588 | 0.77454 |
| 29 | -0.000886 | -0.0069 | 0.502728 |
| 30 | -0.000459 | -0.0036 | 0.501411 |
| 31 | -0.052638 | -0.4077 | 0.65754 |
| 32 | -0.007809 | -0.0605 | 0.524015 |
| 33 | -0.053866 | -0.4172 | 0.661006 |
| 34 | -0.185892 | -1.4399 | 0.922455 |
| 35 | -0.043323 | -0.3356 | 0.630819 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.096846 | 0.7502 | 0.228042 |
| 1 | -0.112375 | -0.8705 | 0.806239 |
| 2 | 0.229605 | 1.7785 | 0.040193 |
| 3 | -0.056871 | -0.4405 | 0.669428 |
| 4 | 0.168135 | 1.3024 | 0.098884 |
| 5 | 0.116858 | 0.9052 | 0.184495 |
| 6 | 0.074303 | 0.5756 | 0.283536 |
| 7 | 0.035807 | 0.2774 | 0.391228 |
| 8 | 0.082267 | 0.6372 | 0.263198 |
| 9 | -0.35732 | -2.7678 | 0.996253 |
| 10 | 0.024405 | 0.189 | 0.425351 |
| 11 | 0.41695 | 3.2297 | 0.001006 |
| 12 | -0.154208 | -1.1945 | 0.881506 |
| 13 | -0.225611 | -1.7476 | 0.957173 |
| 14 | 0.001382 | 0.0107 | 0.495747 |
| 15 | 0.01223 | 0.0947 | 0.46242 |
| 16 | -0.014771 | -0.1144 | 0.545354 |
| 17 | -0.11346 | -0.8789 | 0.808507 |
| 18 | 0.085254 | 0.6604 | 0.255769 |
| 19 | -0.173676 | -1.3453 | 0.908203 |
| 20 | 0.055141 | 0.4271 | 0.335409 |
| 21 | -0.031752 | -0.2459 | 0.596719 |
| 22 | 0.205339 | 1.5906 | 0.058483 |
| 23 | 0.013843 | 0.1072 | 0.457483 |
| 24 | 0.04902 | 0.3797 | 0.352752 |
| 25 | 0.010626 | 0.0823 | 0.467338 |
| 26 | -0.058591 | -0.4538 | 0.67421 |
| 27 | -0.117114 | -0.9072 | 0.816024 |
| 28 | 0.048403 | 0.3749 | 0.354518 |
| 29 | -0.128163 | -0.9927 | 0.83759 |
| 30 | -0.062743 | -0.486 | 0.685634 |
| 31 | -0.060019 | -0.4649 | 0.678158 |
| 32 | -0.030712 | -0.2379 | 0.593614 |
| 33 | 0.094336 | 0.7307 | 0.233896 |
| 34 | -0.075288 | -0.5832 | 0.719021 |
| 35 | -0.142953 | -1.1073 | 0.863709 |
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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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