Stationarity in Time Series-Q2a
| 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 | 7.6811 | 0 |
| 1 | 0.155561 | 1.1949 | 0.118457 |
| 2 | -0.137484 | -1.056 | 0.852371 |
| 3 | -0.5933 | -4.5572 | 0.999987 |
| 4 | -0.190187 | -1.4609 | 0.92532 |
| 5 | 0.076602 | 0.5884 | 0.279257 |
| 6 | 0.354414 | 2.7223 | 0.004255 |
| 7 | 0.130453 | 1.002 | 0.16021 |
| 8 | -0.148666 | -1.1419 | 0.870951 |
| 9 | -0.343398 | -2.6377 | 0.994672 |
| 10 | -0.071319 | -0.5478 | 0.707057 |
| 11 | 0.13913 | 1.0687 | 0.144784 |
| 12 | 0.462136 | 3.5497 | 0.000382 |
| 13 | 0.138215 | 1.0617 | 0.14636 |
| 14 | -0.069581 | -0.5345 | 0.702485 |
| 15 | -0.349955 | -2.6881 | 0.995337 |
| 16 | -0.186817 | -1.435 | 0.921712 |
| 17 | 0.024543 | 0.1885 | 0.42556 |
| 18 | 0.290348 | 2.2302 | 0.014776 |
| 19 | 0.122137 | 0.9382 | 0.175995 |
| 20 | -0.107088 | -0.8226 | 0.792964 |
| 21 | -0.169925 | -1.3052 | 0.901557 |
| 22 | -0.068933 | -0.5295 | 0.700773 |
| 23 | 0.068864 | 0.529 | 0.29941 |
| 24 | 0.140751 | 1.0811 | 0.14202 |
| 25 | 0.079956 | 0.6142 | 0.270738 |
| 26 | -0.057108 | -0.4387 | 0.668743 |
| 27 | -0.105233 | -0.8083 | 0.78892 |
| 28 | -0.094631 | -0.7269 | 0.764912 |
| 29 | 0.006484 | 0.0498 | 0.480224 |
| 30 | 0.154516 | 1.1869 | 0.12002 |
| 31 | 0.069832 | 0.5364 | 0.296854 |
| 32 | -0.05311 | -0.4079 | 0.657605 |
| 33 | -0.144782 | -1.1121 | 0.864695 |
| 34 | -0.049596 | -0.381 | 0.647697 |
| 35 | 0.039119 | 0.3005 | 0.382434 |
| 36 | 0.115361 | 0.8861 | 0.18958 |
| 37 | 0.070262 | 0.5397 | 0.29572 |
| 38 | -0.076963 | -0.5912 | 0.721665 |
| 39 | -0.118026 | -0.9066 | 0.815841 |
| 40 | -0.048231 | -0.3705 | 0.64382 |
| 41 | 0.033227 | 0.2552 | 0.399721 |
| 42 | 0.088556 | 0.6802 | 0.249516 |
| 43 | 0.016862 | 0.1295 | 0.448695 |
| 44 | -0.034831 | -0.2675 | 0.605007 |
| 45 | -0.027796 | -0.2135 | 0.584167 |
| 46 | -0.010601 | -0.0814 | 0.532312 |
| 47 | -0.00647 | -0.0497 | 0.519734 |
| 48 | 0.024773 | 0.1903 | 0.424869 |
| 49 | 0.023295 | 0.1789 | 0.429302 |
| 50 | -0.031227 | -0.2399 | 0.594363 |
| 51 | -0.041611 | -0.3196 | 0.624807 |
| 52 | -0.013 | -0.0999 | 0.539602 |
| 53 | 0.00545 | 0.0419 | 0.483376 |
| 54 | 0.024884 | 0.1911 | 0.424538 |
| 55 | 0.015877 | 0.122 | 0.451675 |
| 56 | -0.003414 | -0.0262 | 0.510415 |
| 57 | -0.013601 | -0.1045 | 0.541425 |
| 58 | -0.004722 | -0.0363 | 0.514404 |
| 59 | NA | NA | NA |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.155561 | 1.1949 | 0.118457 |
| 1 | -0.165693 | -1.2727 | 0.895943 |
| 2 | -0.571746 | -4.3917 | 0.999976 |
| 3 | -0.091409 | -0.7021 | 0.75732 |
| 4 | -0.03208 | -0.2464 | 0.596891 |
| 5 | -0.011913 | -0.0915 | 0.5363 |
| 6 | -0.062445 | -0.4797 | 0.683376 |
| 7 | -0.181967 | -1.3977 | 0.916283 |
| 8 | -0.21655 | -1.6634 | 0.949227 |
| 9 | -0.007371 | -0.0566 | 0.52248 |
| 10 | -0.065722 | -0.5048 | 0.692217 |
| 11 | 0.254232 | 1.9528 | 0.027796 |
| 12 | 0.083774 | 0.6435 | 0.261203 |
| 13 | 0.099507 | 0.7643 | 0.223859 |
| 14 | 0.129063 | 0.9914 | 0.16278 |
| 15 | 0.007563 | 0.0581 | 0.476937 |
| 16 | 0.050018 | 0.3842 | 0.351106 |
| 17 | 0.16005 | 1.2294 | 0.111907 |
| 18 | -0.009079 | -0.0697 | 0.52768 |
| 19 | -0.05196 | -0.3991 | 0.654373 |
| 20 | 0.18051 | 1.3865 | 0.085402 |
| 21 | 0.009307 | 0.0715 | 0.471624 |
| 22 | -0.062895 | -0.4831 | 0.684595 |
| 23 | -0.103922 | -0.7982 | 0.786035 |
| 24 | -0.074431 | -0.5717 | 0.715158 |
| 25 | -0.086783 | -0.6666 | 0.746184 |
| 26 | -0.027919 | -0.2144 | 0.584532 |
| 27 | -0.115488 | -0.8871 | 0.810682 |
| 28 | -0.091989 | -0.7066 | 0.758696 |
| 29 | 0.064782 | 0.4976 | 0.310309 |
| 30 | -0.071942 | -0.5526 | 0.708687 |
| 31 | -0.081888 | -0.629 | 0.73411 |
| 32 | -0.077053 | -0.5919 | 0.721894 |
| 33 | 0.004942 | 0.038 | 0.484925 |
| 34 | 0.006407 | 0.0492 | 0.480457 |
| 35 | 0.010798 | 0.0829 | 0.467089 |
| 36 | 0.062331 | 0.4788 | 0.316936 |
| 37 | 0.025037 | 0.1923 | 0.424077 |
| 38 | -0.058595 | -0.4501 | 0.672847 |
| 39 | 0.014563 | 0.1119 | 0.455656 |
| 40 | -0.017194 | -0.1321 | 0.552311 |
| 41 | -0.074701 | -0.5738 | 0.715854 |
| 42 | -0.054389 | -0.4178 | 0.661183 |
| 43 | -0.050749 | -0.3898 | 0.650959 |
| 44 | 0.067626 | 0.5194 | 0.302696 |
| 45 | 0.000192 | 0.0015 | 0.499414 |
| 46 | -0.078311 | -0.6015 | 0.7251 |
| 47 | -0.062242 | -0.4781 | 0.682823 |
| 48 | -0.017801 | -0.1367 | 0.554147 |
| 49 | -0.006388 | -0.0491 | 0.519486 |
| 50 | 0.012219 | 0.0939 | 0.462771 |
| 51 | -0.004786 | -0.0368 | 0.5146 |
| 52 | 0.020811 | 0.1599 | 0.436772 |
| 53 | 0.051417 | 0.3949 | 0.347154 |
| 54 | -0.000888 | -0.0068 | 0.50271 |
| 55 | 0.013922 | 0.1069 | 0.457602 |
| 56 | -0.016665 | -0.128 | 0.550709 |
| 57 | -0.016916 | -0.1299 | 0.55147 |
| 58 | NA | NA | NA |
| 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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