Q2 Autocorrelation
| 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 | 7.746 | 0 |
| 1 | 0.610069 | 4.7256 | 7e-06 |
| 2 | 0.48991 | 3.7948 | 0.000173 |
| 3 | 0.64607 | 5.0044 | 3e-06 |
| 4 | 0.409083 | 3.1687 | 0.001205 |
| 5 | 0.400578 | 3.1029 | 0.001461 |
| 6 | 0.588461 | 4.5582 | 1.3e-05 |
| 7 | 0.326609 | 2.5299 | 0.007027 |
| 8 | 0.239663 | 1.8564 | 0.034153 |
| 9 | 0.386449 | 2.9934 | 0.002001 |
| 10 | 0.164908 | 1.2774 | 0.103197 |
| 11 | 0.213205 | 1.6515 | 0.051933 |
| 12 | 0.430581 | 3.3353 | 0.000733 |
| 13 | 0.191358 | 1.4822 | 0.071754 |
| 14 | 0.123946 | 0.9601 | 0.170434 |
| 15 | 0.201579 | 1.5614 | 0.061842 |
| 16 | 0.018133 | 0.1405 | 0.444385 |
| 17 | 0.015653 | 0.1212 | 0.451949 |
| 18 | 0.174068 | 1.3483 | 0.091311 |
| 19 | -0.005858 | -0.0454 | 0.51802 |
| 20 | -0.019197 | -0.1487 | 0.558857 |
| 21 | 0.049174 | 0.3809 | 0.352312 |
| 22 | -0.13634 | -1.0561 | 0.852418 |
| 23 | -0.068075 | -0.5273 | 0.700037 |
| 24 | 0.069705 | 0.5399 | 0.29562 |
| 25 | -0.080109 | -0.6205 | 0.731368 |
| 26 | -0.103844 | -0.8044 | 0.787821 |
| 27 | -0.061049 | -0.4729 | 0.680993 |
| 28 | -0.197765 | -1.5319 | 0.934596 |
| 29 | -0.150997 | -1.1696 | 0.87661 |
| 30 | -0.065136 | -0.5045 | 0.692135 |
| 31 | -0.164187 | -1.2718 | 0.895821 |
| 32 | -0.137369 | -1.0641 | 0.854217 |
| 33 | -0.135114 | -1.0466 | 0.850257 |
| 34 | -0.234155 | -1.8138 | 0.962642 |
| 35 | -0.194153 | -1.5039 | 0.931074 |
| 36 | -0.136481 | -1.0572 | 0.852666 |
| 37 | -0.238115 | -1.8444 | 0.964971 |
| 38 | -0.272717 | -2.1125 | 0.980591 |
| 39 | -0.222165 | -1.7209 | 0.954787 |
| 40 | -0.300015 | -2.3239 | 0.988232 |
| 41 | -0.284235 | -2.2017 | 0.984228 |
| 42 | -0.250644 | -1.9415 | 0.97155 |
| 43 | -0.271477 | -2.1029 | 0.980159 |
| 44 | -0.252024 | -1.9522 | 0.972205 |
| 45 | -0.238132 | -1.8446 | 0.96498 |
| 46 | -0.269414 | -2.0869 | 0.979422 |
| 47 | -0.260787 | -2.02 | 0.976075 |
| 48 | -0.209453 | -1.6224 | 0.945022 |
| 49 | -0.216563 | -1.6775 | 0.950675 |
| 50 | -0.215213 | -1.667 | 0.94964 |
| 51 | -0.196894 | -1.5251 | 0.93376 |
| 52 | -0.179153 | -1.3877 | 0.914821 |
| 53 | -0.147878 | -1.1455 | 0.871716 |
| 54 | -0.136282 | -1.0556 | 0.852317 |
| 55 | -0.087724 | -0.6795 | 0.750285 |
| 56 | -0.056317 | -0.4362 | 0.667882 |
| 57 | -0.034322 | -0.2659 | 0.604371 |
| 58 | -0.017669 | -0.1369 | 0.554201 |
| 59 | -0.00218 | -0.0169 | 0.50671 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.610069 | 4.7256 | 7e-06 |
| 1 | 0.187517 | 1.4525 | 0.075787 |
| 2 | 0.476835 | 3.6935 | 0.00024 |
| 3 | -0.257181 | -1.9921 | 0.974543 |
| 4 | 0.219627 | 1.7012 | 0.047039 |
| 5 | 0.230665 | 1.7867 | 0.039517 |
| 6 | -0.261899 | -2.0287 | 0.976531 |
| 7 | -0.079309 | -0.6143 | 0.729339 |
| 8 | 0.048708 | 0.3773 | 0.353646 |
| 9 | -0.139656 | -1.0818 | 0.858158 |
| 10 | 0.249217 | 1.9304 | 0.029142 |
| 11 | 0.154343 | 1.1955 | 0.118291 |
| 12 | -0.127672 | -0.9889 | 0.836669 |
| 13 | -0.097852 | -0.758 | 0.774278 |
| 14 | -0.222957 | -1.727 | 0.955345 |
| 15 | 0.02628 | 0.2036 | 0.419691 |
| 16 | -0.083146 | -0.644 | 0.739 |
| 17 | 0.059638 | 0.462 | 0.322893 |
| 18 | 0.014936 | 0.1157 | 0.454141 |
| 19 | 0.108129 | 0.8376 | 0.202799 |
| 20 | -0.09294 | -0.7199 | 0.762812 |
| 21 | -0.060309 | -0.4672 | 0.678958 |
| 22 | 0.033702 | 0.2611 | 0.397472 |
| 23 | -0.023981 | -0.1858 | 0.573368 |
| 24 | -0.030098 | -0.2331 | 0.591777 |
| 25 | -0.086804 | -0.6724 | 0.748039 |
| 26 | -0.038219 | -0.296 | 0.615889 |
| 27 | 0.067061 | 0.5194 | 0.302679 |
| 28 | 0.017823 | 0.1381 | 0.44533 |
| 29 | -0.063701 | -0.4934 | 0.688244 |
| 30 | 0.067368 | 0.5218 | 0.301855 |
| 31 | -0.076859 | -0.5953 | 0.723074 |
| 32 | -0.052458 | -0.4063 | 0.657029 |
| 33 | 0.020811 | 0.1612 | 0.436237 |
| 34 | -0.105839 | -0.8198 | 0.792219 |
| 35 | -0.014779 | -0.1145 | 0.545381 |
| 36 | -0.13815 | -1.0701 | 0.85557 |
| 37 | -0.159904 | -1.2386 | 0.889843 |
| 38 | 0.216743 | 1.6789 | 0.049188 |
| 39 | -0.042706 | -0.3308 | 0.629025 |
| 40 | 0.000816 | 0.0063 | 0.497489 |
| 41 | -0.076542 | -0.5929 | 0.72226 |
| 42 | 0.016131 | 0.125 | 0.450489 |
| 43 | 0.01759 | 0.1363 | 0.446038 |
| 44 | -0.054214 | -0.4199 | 0.661985 |
| 45 | -0.009846 | -0.0763 | 0.530271 |
| 46 | -0.032293 | -0.2501 | 0.598333 |
| 47 | 0.008034 | 0.0622 | 0.475291 |
| 48 | 0.080905 | 0.6267 | 0.26662 |
| 49 | 0.038273 | 0.2965 | 0.383951 |
| 50 | 0.003448 | 0.0267 | 0.489389 |
| 51 | 0.016747 | 0.1297 | 0.44861 |
| 52 | 0.03991 | 0.3091 | 0.379143 |
| 53 | -0.026739 | -0.2071 | 0.581692 |
| 54 | -0.024084 | -0.1866 | 0.57368 |
| 55 | 0.104998 | 0.8133 | 0.209629 |
| 56 | 0.02807 | 0.2174 | 0.414305 |
| 57 | 0.01373 | 0.1064 | 0.45783 |
| 58 | -0.021621 | -0.1675 | 0.566221 |
| 59 | NA | NA | NA |
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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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