Autocorrelatiefunctie (met degree aangepast) 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 12:54:27 +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/t130390873043iid51flvuhr1m.htm/, Retrieved Wed, 27 Apr 2011 14:52:13 +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 3 seconds R Server 'Gwilym Jenkins' @ www.wessa.org Autocorrelation Function Time lag k ACF(k) T-STAT P-value 1 0.294032 2.6788 0.004454 2 -0.039784 -0.3624 0.35897 3 0.121011 1.1025 0.136724 4 0.05116 0.4661 0.321187 5 -0.023507 -0.2142 0.415475 6 -0.11348 -1.0339 0.152104 7 -0.341757 -3.1136 0.001268 8 -0.219098 -1.9961 0.024602 9 -0.100184 -0.9127 0.182018 10 -0.112326 -1.0233 0.15456 11 -0.125533 -1.1437 0.128026 12 -0.14794 -1.3478 0.090696 13 -0.045492 -0.4145 0.339806 14 0.077372 0.7049 0.241427 15 0.167076 1.5221 0.065888 16 0.190085 1.7318 0.043516 17 0.039148 0.3567 0.361129 18 0.088457 0.8059 0.211306 19 0.21365 1.9464 0.027492 20 0.087377 0.796 0.214138 21 -0.026425 -0.2407 0.405175 22 -3.5e-05 -3e-04 0.499872 23 -0.173742 -1.5829 0.058628 24 -0.069514 -0.6333 0.264139 25 -0.041318 -0.3764 0.35378 26 -0.106041 -0.9661 0.168406 27 -0.039706 -0.3617 0.359233 28 -0.011945 -0.1088 0.456804 29 -0.127028 -1.1573 0.125239 30 -0.024579 -0.2239 0.411683 31 0.031247 0.2847 0.388303 32 -0.059155 -0.5389 0.295691 33 -0.095896 -0.8737 0.192413 34 -0.025631 -0.2335 0.40797 35 0.121712 1.1088 0.13535 36 0.097648 0.8896 0.188121 37 -0.043301 -0.3945 0.347114 38 -0.056969 -0.519 0.302567 39 0.104068 0.9481 0.172915 40 0.216865 1.9757 0.025754 41 0.066554 0.6063 0.272974 42 -0.056433 -0.5141 0.304264 43 0.001913 0.0174 0.493068 44 -0.005029 -0.0458 0.481784 45 -0.050293 -0.4582 0.324007 46 -0.098844 -0.9005 0.185228 47 -0.174113 -1.5862 0.058243 48 -0.145116 -1.3221 0.094889 Partial Autocorrelation Function Time lag k PACF(k) T-STAT P-value 1 0.294032 2.6788 0.004454 2 -0.138185 -1.2589 0.105793 3 0.195241 1.7787 0.039472 4 -0.067414 -0.6142 0.27039 5 0.01418 0.1292 0.448761 6 -0.148494 -1.3528 0.089889 7 -0.307757 -2.8038 0.003143 8 -0.043977 -0.4006 0.344854 9 -0.094957 -0.8651 0.194738 10 0.006887 0.0627 0.475059 11 -0.083692 -0.7625 0.223971 12 -0.126108 -1.1489 0.126948 13 -0.033477 -0.305 0.380569 14 -0.048836 -0.4449 0.328769 15 0.129683 1.1815 0.120395 16 0.088598 0.8072 0.21094 17 -0.09186 -0.8369 0.20253 18 0.04338 0.3952 0.34685 19 0.027958 0.2547 0.39979 20 -0.009257 -0.0843 0.466498 21 -0.013045 -0.1188 0.452843 22 0.060699 0.553 0.290876 23 -0.172664 -1.573 0.059756 24 0.063456 0.5781 0.282377 25 -0.067703 -0.6168 0.269527 26 0.091631 0.8348 0.203114 27 0.068147 0.6209 0.268199 28 0.008816 0.0803 0.468089 29 -0.137951 -1.2568 0.106176 30 -0.063188 -0.5757 0.283198 31 -0.01481 -0.1349 0.446497 32 -0.086402 -0.7872 0.216715 33 -0.095755 -0.8724 0.192761 34 -0.059467 -0.5418 0.294714 35 0.072551 0.661 0.255229 36 -0.021843 -0.199 0.421377 37 -0.09723 -0.8858 0.18914 38 -0.080438 -0.7328 0.232866 39 0.127398 1.1607 0.124556 40 0.131704 1.1999 0.116799 41 -0.041382 -0.377 0.353564 42 -0.011814 -0.1076 0.457276 43 -0.026155 -0.2383 0.406123 44 -0.102048 -0.9297 0.177612 45 -0.014816 -0.135 0.446478 46 -0.138281 -1.2598 0.105636 47 0.010226 0.0932 0.462999 48 -0.041757 -0.3804 0.352303

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Apr/27/t130390873043iid51flvuhr1m/1cyaq1303908864.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/27/t130390873043iid51flvuhr1m/1cyaq1303908864.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/27/t130390873043iid51flvuhr1m/204ru1303908864.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/27/t130390873043iid51flvuhr1m/204ru1303908864.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/27/t130390873043iid51flvuhr1m/3hcjq1303908864.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/27/t130390873043iid51flvuhr1m/3hcjq1303908864.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 48 ; par2 = 1 ; par3 = 1 ; 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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