Autocorrelatiefunctie wisselkoers

*Unverified author*

R Software Module: /rwasp_autocorrelation.wasp (opens new window with default values)

Title produced by software: (Partial) Autocorrelation Function

Date of computation: Wed, 27 Apr 2011 12:48:38 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/Apr/27/t1303908415s762phv8xzzbmws.htm/, Retrieved Wed, 27 Apr 2011 14:47:07 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W12

Dataseries X:

» Textbox « » Textfile « » CSV «

1,2638 1,2640 1,2261 1,1989 1,2000 1,2146 1,2266 1,2191 1,2224 1,2507 1,2997 1,3406 1,3123 1,3013 1,3185 1,2943 1,2697 1,2155 1,2041 1,2295 1,2234 1,2022 1,1789 1,1861 1,2126 1,1940 1,2028 1,2273 1,2767 1,2661 1,2681 1,2810 1,2722 1,2617 1,2888 1,3205 1,2993 1,3080 1,3246 1,3513 1,3518 1,3421 1,3726 1,3626 1,3910 1,4233 1,4683 1,4559 1,4728 1,4759 1,5520 1,5754 1,5554 1,5562 1,5759 1,4955 1,4342 1,3266 1,2744 1,3511 1,3244 1,2797 1,3050 1,3199 1,3646 1,4014 1,4092 1,4266 1,4575 1,4821 1,4908 1,4579 1,4266 1,3680 1,3570 1,3417 1,2563 1,2223 1,2811 1,2903 1,3103 1,3901 1,3654 1,3221

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'Gwilym Jenkins' @ www.wessa.org Autocorrelation Function Time lag k ACF(k) T-STAT P-value 1 0.938418 8.6007 0 2 0.842437 7.7211 0 3 0.748335 6.8586 0 4 0.637089 5.839 0 5 0.51964 4.7626 4e-06 6 0.405944 3.7205 0.000179 7 0.306052 2.805 0.003125 8 0.246183 2.2563 0.013324 9 0.213062 1.9527 0.02709 10 0.193969 1.7778 0.039532 11 0.191911 1.7589 0.041119 12 0.208225 1.9084 0.029877 13 0.239982 2.1995 0.015296 14 0.276389 2.5332 0.006581 15 0.304763 2.7932 0.003231 16 0.311213 2.8523 0.002731 17 0.292943 2.6849 0.00437 18 0.265593 2.4342 0.008522 19 0.226692 2.0777 0.020397 20 0.164076 1.5038 0.068194 21 0.090205 0.8267 0.205363 22 0.017608 0.1614 0.436092 23 -0.057079 -0.5231 0.301127 24 -0.110449 -1.0123 0.157154 25 -0.153486 -1.4067 0.081599 26 -0.19381 -1.7763 0.039653 27 -0.22061 -2.0219 0.023183 28 -0.240107 -2.2006 0.015254 29 -0.254084 -2.3287 0.011138 30 -0.253454 -2.3229 0.0113 31 -0.250192 -2.293 0.012172 32 -0.249881 -2.2902 0.012258 33 -0.243354 -2.2304 0.014194 34 -0.226768 -2.0784 0.020364 35 -0.205916 -1.8873 0.03129 36 -0.197179 -1.8072 0.037157 37 -0.201882 -1.8503 0.033895 38 -0.200854 -1.8409 0.034587 39 -0.192324 -1.7627 0.040796 40 -0.194372 -1.7814 0.039227 41 -0.222254 -2.037 0.0224 42 -0.258696 -2.371 0.010015 43 -0.286408 -2.625 0.005147 44 -0.315043 -2.8874 0.002469 45 -0.340911 -3.1245 0.001222 46 -0.358551 -3.2862 0.000742 47 -0.361125 -3.3098 0.000689 48 -0.34407 -3.1534 0.001119 Partial Autocorrelation Function Time lag k PACF(k) T-STAT P-value 1 0.938418 8.6007 0 2 -0.319933 -2.9322 0.002168 3 0.047423 0.4346 0.332471 4 -0.249578 -2.2874 0.012343 5 -0.019556 -0.1792 0.429093 6 -0.072031 -0.6602 0.255474 7 0.066217 0.6069 0.27278 8 0.24786 2.2717 0.012831 9 0.026699 0.2447 0.403644 10 0.049886 0.4572 0.324348 11 -0.007279 -0.0667 0.473485 12 0.060278 0.5525 0.29105 13 0.069425 0.6363 0.263159 14 0.020676 0.1895 0.425081 15 0.005838 0.0535 0.478729 16 -0.153691 -1.4086 0.081322 17 -0.127415 -1.1678 0.1231 18 0.023677 0.217 0.414365 19 -0.023634 -0.2166 0.414518 20 -0.076283 -0.6991 0.243195 21 -0.024645 -0.2259 0.410923 22 -0.030315 -0.2778 0.390908 23 -0.124738 -1.1432 0.128093 24 0.135288 1.2399 0.109226 25 -0.093359 -0.8556 0.197314 26 -0.01683 -0.1543 0.43889 27 -0.098993 -0.9073 0.183424 28 -0.129833 -1.1899 0.118711 29 -0.023533 -0.2157 0.41488 30 0.028024 0.2568 0.398965 31 0.037488 0.3436 0.366008 32 -0.031732 -0.2908 0.385948 33 0.019531 0.179 0.429183 34 0.063231 0.5795 0.281894 35 0.050247 0.4605 0.323165 36 -0.06655 -0.6099 0.271774 37 -0.061093 -0.5599 0.288509 38 0.083729 0.7674 0.222502 39 0.022068 0.2023 0.420102 40 -0.098558 -0.9033 0.184475 41 -0.19174 -1.7573 0.041253 42 0.000352 0.0032 0.498718 43 0.00659 0.0604 0.475989 44 -0.056339 -0.5164 0.303481 45 0.121672 1.1151 0.133986 46 -0.028986 -0.2657 0.395574 47 0.135688 1.2436 0.108553 48 -0.040241 -0.3688 0.356598

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Apr/27/t1303908415s762phv8xzzbmws/19j5y1303908516.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/27/t1303908415s762phv8xzzbmws/19j5y1303908516.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/27/t1303908415s762phv8xzzbmws/29efk1303908516.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/27/t1303908415s762phv8xzzbmws/29efk1303908516.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/27/t1303908415s762phv8xzzbmws/3gpto1303908516.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/27/t1303908415s762phv8xzzbmws/3gpto1303908516.ps (open in new window)

Parameters (Session):

par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

R code (references can be found in the software module):

if (par1 == 'Default') { 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 (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma' par7 <- as.numeric(par7) if (par8 != '') par8 <- as.numeric(par8) ox <- x if (par8 == '') { if (par2 == 0) { x <- log(x) } else { x <- (x ^ par2 - 1) / par2 } } else { x <- log(x,base=par8) } if (par3 > 0) x <- diff(x,lag=1,difference=par3) if (par4 > 0) x <- diff(x,lag=par5,difference=par4) bitmap(file='picts.png') op <- par(mfrow=c(2,1)) plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value') if (par8=='') { mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='') mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') } else { mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='') mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') } plot(x,type='l', main=mytitle,xlab='time',ylab='value') par(op) dev.off() bitmap(file='pic1.png') racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub) dev.off() bitmap(file='pic2.png') rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub) 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 2:(par1+1)) { 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(abs(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,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(abs(mytstat),lengthx),6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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