Autocorrelatiefunctie 2-Consumentenprijzen Kabeljauw-Toon Baeten

*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, 18 May 2011 15:01:05 +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/May/18/t1305730790uyz15lkmzf97z5x.htm/, Retrieved Wed, 18 May 2011 16:59:54 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W12

Dataseries X:

» Textbox « » Textfile « » CSV «

12.32 12.34 12.36 12.54 12.77 12.79 12.96 12.96 13 13.19 13.25 13.61 13.8 13.83 14.04 14.16 14.2 14.27 14.31 14.69 14.9 14.92 15.01 15.09 15.14 15.24 15.33 15.36 15.44 15.5 15.58 15.65 15.72 15.82 15.87 16.07 16.18 16.19 16.39 16.54 16.61 16.62 16.66 16.71 16.72 16.79 16.82 16.83 16.91 16.97 17.02 17.03 17.04 17.07 17.11 17.12 17.14 17.18 17.24 17.26 17.26 17.29 17.36 17.44 17.48 17.48 17.52 17.54 17.58 17.64 17.69 17.69 17.76 17.79 17.82 17.89 17.95 18 18.03 18.06 18.08 18.13 18.16 18.18 18.18 18.27 18.31 18.35 18.45 18.5

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 'George Udny Yule' @ 216.218.223.82 Autocorrelation Function Time lag k ACF(k) T-STAT P-value 1 0.207876 1.9611 0.026497 2 0.081577 0.7696 0.221789 3 0.276785 2.6112 0.005294 4 0.023973 0.2262 0.410798 5 0.308014 2.9058 0.002311 6 0.145368 1.3714 0.08685 7 0.152013 1.4341 0.077525 8 0.406988 3.8395 0.000115 9 0.114794 1.083 0.140875 10 0.142866 1.3478 0.090573 11 0.144118 1.3596 0.088694 12 -0.044797 -0.4226 0.3368 13 0.122814 1.1586 0.124854 14 0.084349 0.7958 0.214147 15 0.145656 1.3741 0.086428 16 0.276099 2.6047 0.005388 17 0.003119 0.0294 0.488297 18 -0.044552 -0.4203 0.33764 19 0.183827 1.7342 0.04317 20 0.087589 0.8263 0.205418 21 0.030773 0.2903 0.386125 22 -0.007291 -0.0688 0.472658 23 0.025235 0.2381 0.406187 24 0.168644 1.591 0.05758 25 0.002277 0.0215 0.491453 26 0.028495 0.2688 0.394345 27 0.088782 0.8376 0.202259 28 -0.002838 -0.0268 0.489349 29 0.061954 0.5845 0.280192 30 -0.039658 -0.3741 0.354598 31 -0.032652 -0.308 0.379385 32 -0.049887 -0.4706 0.319528 33 -0.119545 -1.1278 0.131222 34 -0.006124 -0.0578 0.477028 35 -0.052353 -0.4939 0.311299 36 -0.079691 -0.7518 0.227076 37 -0.073743 -0.6957 0.244219 38 -0.107652 -1.0156 0.156289 39 -0.045188 -0.4263 0.335458 40 -0.128932 -1.2163 0.113536 41 -0.165801 -1.5642 0.060665 42 -0.091191 -0.8603 0.195968 43 -0.076085 -0.7178 0.237385 44 -0.088503 -0.8349 0.202996 45 -0.134867 -1.2723 0.103285 46 -0.13183 -1.2437 0.108442 47 -0.095445 -0.9004 0.185161 48 -0.157687 -1.4876 0.070193 Partial Autocorrelation Function Time lag k PACF(k) T-STAT P-value 1 0.207876 1.9611 0.026497 2 0.040097 0.3783 0.353063 3 0.263985 2.4904 0.007308 4 -0.092471 -0.8724 0.192677 5 0.343856 3.2439 0.000831 6 -0.083226 -0.7852 0.217224 7 0.232151 2.1901 0.015566 8 0.199501 1.8821 0.031546 9 0.040941 0.3862 0.350122 10 0.009693 0.0914 0.463671 11 -0.032976 -0.3111 0.378229 12 -0.138929 -1.3107 0.096673 13 -0.05741 -0.5416 0.29472 14 -0.029599 -0.2792 0.390355 15 0.096876 0.9139 0.181613 16 0.105401 0.9943 0.161374 17 -0.064182 -0.6055 0.273195 18 -0.146271 -1.3799 0.085534 19 0.20227 1.9082 0.029793 20 0.053228 0.5021 0.308401 21 -0.036683 -0.3461 0.365054 22 -0.132623 -1.2512 0.107077 23 0.008231 0.0777 0.469139 24 -0.009116 -0.086 0.465831 25 -0.03129 -0.2952 0.38427 26 0.076327 0.7201 0.236684 27 -0.004596 -0.0434 0.482758 28 0.028252 0.2665 0.395226 29 -0.008434 -0.0796 0.468382 30 -0.088259 -0.8326 0.203639 31 -0.024013 -0.2265 0.410651 32 -0.189027 -1.7833 0.038975 33 -0.029373 -0.2771 0.39117 34 -0.089275 -0.8422 0.200961 35 -0.10542 -0.9945 0.16133 36 -0.070469 -0.6648 0.253946 37 0.057727 0.5446 0.293696 38 0.066778 0.63 0.265162 39 -0.008752 -0.0826 0.467189 40 -0.038751 -0.3656 0.357776 41 -0.000828 -0.0078 0.496891 42 -0.024058 -0.227 0.410485 43 5.9e-05 6e-04 0.499778 44 -0.081485 -0.7687 0.222045 45 -0.05184 -0.4891 0.313004 46 -0.052539 -0.4957 0.310681 47 0.02595 0.2448 0.403583 48 -0.061318 -0.5785 0.282202

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/18/t1305730790uyz15lkmzf97z5x/1owel1305730862.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305730790uyz15lkmzf97z5x/1owel1305730862.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305730790uyz15lkmzf97z5x/2rbcd1305730862.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305730790uyz15lkmzf97z5x/2rbcd1305730862.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305730790uyz15lkmzf97z5x/3h67y1305730862.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305730790uyz15lkmzf97z5x/3h67y1305730862.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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