Stationarity in Time Series-Q2
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 1 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 | 0.683209 | 5.2921 | 1e-06 |
| 2 | 0.305606 | 2.3672 | 0.010583 |
| 3 | 0.030111 | 0.2332 | 0.408186 |
| 4 | 0.07098 | 0.5498 | 0.292246 |
| 5 | 0.190235 | 1.4736 | 0.072915 |
| 6 | 0.276793 | 2.144 | 0.018046 |
| 7 | 0.182804 | 1.416 | 0.080973 |
| 8 | 0.010537 | 0.0816 | 0.467612 |
| 9 | -0.04852 | -0.3758 | 0.645816 |
| 10 | 0.092035 | 0.7129 | 0.239335 |
| 11 | 0.239087 | 1.852 | 0.034477 |
| 12 | 0.327722 | 2.5385 | 0.006872 |
| 13 | 0.174332 | 1.3504 | 0.090984 |
| 14 | -0.054169 | -0.4196 | 0.661858 |
| 15 | -0.234025 | -1.8127 | 0.962563 |
| 16 | -0.230138 | -1.7826 | 0.960148 |
| 17 | -0.109659 | -0.8494 | 0.800486 |
| 18 | -0.02081 | -0.1612 | 0.56376 |
| 19 | -0.092487 | -0.7164 | 0.761739 |
| 20 | -0.212968 | -1.6496 | 0.947879 |
| 21 | -0.234083 | -1.8132 | 0.962598 |
| 22 | -0.1594 | -1.2347 | 0.889123 |
| 23 | -0.071856 | -0.5566 | 0.710062 |
| 24 | -0.033649 | -0.2606 | 0.602371 |
| 25 | -0.060143 | -0.4659 | 0.678499 |
| 26 | -0.122607 | -0.9497 | 0.826965 |
| 27 | -0.149207 | -1.1557 | 0.873817 |
| 28 | -0.126256 | -0.978 | 0.833994 |
| 29 | -0.101522 | -0.7864 | 0.782632 |
| 30 | -0.079104 | -0.6127 | 0.728818 |
| 31 | -0.142154 | -1.1011 | 0.862379 |
| 32 | -0.213272 | -1.652 | 0.94812 |
| 33 | -0.238248 | -1.8455 | 0.965046 |
| 34 | -0.182575 | -1.4142 | 0.918767 |
| 35 | -0.124425 | -0.9638 | 0.830491 |
| 36 | -0.090212 | -0.6988 | 0.756306 |
| 37 | -0.107121 | -0.8298 | 0.795016 |
| 38 | -0.106642 | -0.826 | 0.793975 |
| 39 | -0.059117 | -0.4579 | 0.675666 |
| 40 | 0.037187 | 0.288 | 0.387151 |
| 41 | 0.027734 | 0.2148 | 0.415316 |
| 42 | -0.012791 | -0.0991 | 0.539297 |
| 43 | -0.095677 | -0.7411 | 0.769241 |
| 44 | -0.092616 | -0.7174 | 0.762044 |
| 45 | -0.065565 | -0.5079 | 0.693295 |
| 46 | -0.021767 | -0.1686 | 0.566664 |
| 47 | 0.010129 | 0.0785 | 0.468861 |
| 48 | 0.03726 | 0.2886 | 0.386936 |
| 49 | 0.063591 | 0.4926 | 0.312055 |
| 50 | 0.058079 | 0.4499 | 0.32721 |
| 51 | 0.075624 | 0.5858 | 0.280109 |
| 52 | 0.098042 | 0.7594 | 0.225284 |
| 53 | 0.082332 | 0.6377 | 0.263034 |
| 54 | 0.056238 | 0.4356 | 0.332338 |
| 55 | 0.020586 | 0.1595 | 0.436922 |
| 56 | 0.004849 | 0.0376 | 0.48508 |
| 57 | 0.003384 | 0.0262 | 0.489587 |
| 58 | 0.01864 | 0.1444 | 0.442841 |
| 59 | 0.015657 | 0.1213 | 0.451939 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.683209 | 5.2921 | 1e-06 |
| 1 | -0.302253 | -2.3412 | 0.98872 |
| 2 | -0.072832 | -0.5642 | 0.712623 |
| 3 | 0.346888 | 2.687 | 0.004657 |
| 4 | 0.020682 | 0.1602 | 0.436629 |
| 5 | 0.036358 | 0.2816 | 0.389599 |
| 6 | -0.089515 | -0.6934 | 0.754626 |
| 7 | -0.095337 | -0.7385 | 0.768448 |
| 8 | 0.157492 | 1.2199 | 0.113633 |
| 9 | 0.195833 | 1.5169 | 0.06727 |
| 10 | -0.048003 | -0.3718 | 0.644336 |
| 11 | 0.148682 | 1.1517 | 0.127009 |
| 12 | -0.211622 | -1.6392 | 0.946799 |
| 13 | -0.141639 | -1.0971 | 0.861517 |
| 14 | -0.047248 | -0.366 | 0.642168 |
| 15 | -0.140849 | -1.091 | 0.860185 |
| 16 | 0.050395 | 0.3904 | 0.348828 |
| 17 | -0.027193 | -0.2106 | 0.583059 |
| 18 | -0.208364 | -1.614 | 0.94411 |
| 19 | 0.069979 | 0.5421 | 0.294894 |
| 20 | 0.07018 | 0.5436 | 0.294362 |
| 21 | -0.200154 | -1.5504 | 0.936846 |
| 22 | 0.032778 | 0.2539 | 0.400221 |
| 23 | -0.02236 | -0.1732 | 0.568462 |
| 24 | 0.060485 | 0.4685 | 0.320558 |
| 25 | 0.141353 | 1.0949 | 0.138964 |
| 26 | -0.081524 | -0.6315 | 0.734939 |
| 27 | -0.01223 | -0.0947 | 0.53758 |
| 28 | 0.011912 | 0.0923 | 0.463395 |
| 29 | -0.069456 | -0.538 | 0.703717 |
| 30 | -0.148624 | -1.1512 | 0.872899 |
| 31 | -0.027424 | -0.2124 | 0.583753 |
| 32 | -0.064751 | -0.5016 | 0.691093 |
| 33 | -0.008941 | -0.0693 | 0.527491 |
| 34 | -0.087893 | -0.6808 | 0.750697 |
| 35 | -0.055349 | -0.4287 | 0.665174 |
| 36 | -0.020471 | -0.1586 | 0.562729 |
| 37 | 0.036235 | 0.2807 | 0.389962 |
| 38 | 0.021405 | 0.1658 | 0.434435 |
| 39 | 0.09289 | 0.7195 | 0.237307 |
| 40 | -0.076669 | -0.5939 | 0.722587 |
| 41 | 0.045034 | 0.3488 | 0.364217 |
| 42 | -0.006166 | -0.0478 | 0.518966 |
| 43 | 0.039131 | 0.3031 | 0.381428 |
| 44 | 0.001424 | 0.011 | 0.495618 |
| 45 | -0.112684 | -0.8728 | 0.806886 |
| 46 | 0.097302 | 0.7537 | 0.22699 |
| 47 | 0.020916 | 0.162 | 0.43592 |
| 48 | 0.00234 | 0.0181 | 0.492799 |
| 49 | -0.100531 | -0.7787 | 0.780394 |
| 50 | -0.037769 | -0.2926 | 0.614566 |
| 51 | -0.044679 | -0.3461 | 0.634754 |
| 52 | -0.012371 | -0.0958 | 0.538011 |
| 53 | -0.016556 | -0.1282 | 0.550806 |
| 54 | -0.000108 | -8e-04 | 0.500331 |
| 55 | -0.004294 | -0.0333 | 0.51321 |
| 56 | -0.050633 | -0.3922 | 0.651852 |
| 57 | 0.001031 | 0.008 | 0.496829 |
| 58 | -0.033397 | -0.2587 | 0.601622 |
| 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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