Autocorrelatie - Niet duurzame consumptiegoederen
| 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 | 'Gwilym Jenkins' @ 72.249.127.135 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 6.9282 | 0 |
| 1 | 0.144005 | 0.9977 | 0.161714 |
| 2 | 0.05347 | 0.3705 | 0.356337 |
| 3 | 0.201314 | 1.3947 | 0.084757 |
| 4 | 0.009045 | 0.0627 | 0.475147 |
| 5 | 0.076541 | 0.5303 | 0.299177 |
| 6 | 0.144096 | 0.9983 | 0.161563 |
| 7 | -0.163556 | -1.1331 | 0.86861 |
| 8 | 0.005203 | 0.036 | 0.485696 |
| 9 | 0.185607 | 1.2859 | 0.10232 |
| 10 | -0.158944 | -1.1012 | 0.861848 |
| 11 | -0.115718 | -0.8017 | 0.786666 |
| 12 | -0.178473 | -1.2365 | 0.888855 |
| 13 | -0.111832 | -0.7748 | 0.778869 |
| 14 | 0.096786 | 0.6706 | 0.252859 |
| 15 | -0.114129 | -0.7907 | 0.783499 |
| 16 | -0.127459 | -0.8831 | 0.809197 |
| 17 | 0.108602 | 0.7524 | 0.227737 |
| 18 | -0.016244 | -0.1125 | 0.544569 |
| 19 | -0.146798 | -1.017 | 0.842884 |
| 20 | 0.012251 | 0.0849 | 0.466356 |
| 21 | -0.067651 | -0.4687 | 0.679298 |
| 22 | -0.122562 | -0.8491 | 0.799988 |
| 23 | 0.07911 | 0.5481 | 0.293084 |
| 24 | -0.251648 | -1.7435 | 0.956173 |
| 25 | -0.180541 | -1.2508 | 0.89147 |
| 26 | -0.047742 | -0.3308 | 0.628868 |
| 27 | -0.008267 | -0.0573 | 0.522719 |
| 28 | -0.045161 | -0.3129 | 0.622137 |
| 29 | 0.074891 | 0.5189 | 0.303121 |
| 30 | 0.008999 | 0.0623 | 0.475272 |
| 31 | 0.022473 | 0.1557 | 0.438461 |
| 32 | 0.042432 | 0.294 | 0.385022 |
| 33 | -0.128348 | -0.8892 | 0.81084 |
| 34 | 0.060175 | 0.4169 | 0.339304 |
| 35 | 0.030185 | 0.2091 | 0.417616 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.144005 | 0.9977 | 0.161714 |
| 1 | 0.033426 | 0.2316 | 0.408923 |
| 2 | 0.193277 | 1.3391 | 0.093428 |
| 3 | -0.0487 | -0.3374 | 0.631359 |
| 4 | 0.074741 | 0.5178 | 0.303482 |
| 5 | 0.091326 | 0.6327 | 0.264958 |
| 6 | -0.204731 | -1.4184 | 0.918736 |
| 7 | 0.031142 | 0.2158 | 0.415046 |
| 8 | 0.164037 | 1.1365 | 0.130698 |
| 9 | -0.170365 | -1.1803 | 0.878157 |
| 10 | -0.118721 | -0.8225 | 0.792576 |
| 11 | -0.206857 | -1.4331 | 0.920851 |
| 12 | 0.061914 | 0.429 | 0.334938 |
| 13 | 0.12414 | 0.8601 | 0.197014 |
| 14 | -0.140758 | -0.9752 | 0.832825 |
| 15 | 0.020772 | 0.1439 | 0.443085 |
| 16 | 0.128786 | 0.8923 | 0.188354 |
| 17 | -0.036418 | -0.2523 | 0.599062 |
| 18 | -0.196132 | -1.3588 | 0.909726 |
| 19 | 0.02419 | 0.1676 | 0.433804 |
| 20 | 0.121709 | 0.8432 | 0.201643 |
| 21 | -0.236708 | -1.64 | 0.946224 |
| 22 | -0.07039 | -0.4877 | 0.686001 |
| 23 | -0.175556 | -1.2163 | 0.885089 |
| 24 | -0.025686 | -0.178 | 0.570248 |
| 25 | -0.14809 | -1.026 | 0.844983 |
| 26 | 0.097581 | 0.6761 | 0.251122 |
| 27 | 0.124273 | 0.861 | 0.196762 |
| 28 | 0.123354 | 0.8546 | 0.198503 |
| 29 | -0.023829 | -0.1651 | 0.565219 |
| 30 | -0.080714 | -0.5592 | 0.710688 |
| 31 | 0.001911 | 0.0132 | 0.494744 |
| 32 | -0.112983 | -0.7828 | 0.781196 |
| 33 | -0.051022 | -0.3535 | 0.637367 |
| 34 | -0.05773 | -0.4 | 0.654521 |
| 35 | -0.028493 | -0.1974 | 0.577829 |
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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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Software written by Ed van Stee & Patrick Wessa
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