inflation in consumer prices (%)

*Unverified author*

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

Title produced by software: (Partial) Autocorrelation Function

Date of computation: Thu, 19 May 2011 17:05:00 +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/19/t1305824510c77l48x89ftafzb.htm/, Retrieved Thu, 19 May 2011 19:01:50 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W12

Dataseries X:

» Textbox « » Textfile « » CSV «

0.440 0.548 0.163 0.381 0.164 0.109 0.328 0.435 0.325 0.108 0.054 0.270 0.431 0.215 0.214 0.160 0.427 0.372 0.106 0.053 0.317 0.527 0.472 0.000 0.052 0.418 0.364 0.311 0.052 0.052 0.620 0.616 1.377 0.151 0.502 0.000 0.606 0.050 0.150 0.501 0.299 0.248 0.545 0.444 0.491 0.444 0.050 0.545 0.138 0.423 0.495 0.370 0.388 0.169 0.241 0.014 0.376 0.331 0.789 0.289 0.359 0.236 0.367 0.309 0.551 0.901 0.870 0.160 0.032 0.877 1.812 0.784 0.270 0.462 0.146 0.108 0.132 0.680 0.117 0.345 0.204 0.227 0.236 0.092 0.138 0.046 0.023 0.009 0.142 0.207 0.346 0.207 0.165 0.247 0.123 0.433

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Autocorrelation Function Time lag k ACF(k) T-STAT P-value 1 0.2747 2.6915 0.004196 2 0.024816 0.2431 0.404205 3 -0.138797 -1.3599 0.088519 4 0.148619 1.4562 0.074305 5 0.149085 1.4607 0.073677 6 0.089559 0.8775 0.191205 7 0.103226 1.0114 0.157183 8 0.02625 0.2572 0.398788 9 -0.026102 -0.2557 0.399346 10 -0.046058 -0.4513 0.326404 11 0.053041 0.5197 0.302237 12 -0.00757 -0.0742 0.470516 13 0.011427 0.112 0.455544 14 -0.083418 -0.8173 0.207882 15 -0.07504 -0.7352 0.231994 16 -0.194507 -1.9058 0.029836 17 -0.181678 -1.7801 0.039114 18 -0.063461 -0.6218 0.267779 19 0.012379 0.1213 0.451859 20 0.024126 0.2364 0.406818 21 -0.121738 -1.1928 0.117947 22 -0.110727 -1.0849 0.140343 23 -0.142457 -1.3958 0.082999 24 -0.073142 -0.7166 0.237668 25 -0.01795 -0.1759 0.430383 26 0.10371 1.0161 0.156058 27 -0.008131 -0.0797 0.468333 28 -0.017691 -0.1733 0.431377 29 -0.075103 -0.7359 0.231807 30 0.014318 0.1403 0.444363 31 0.034337 0.3364 0.36864 32 0.004855 0.0476 0.48108 33 0.048082 0.4711 0.319316 34 0.061921 0.6067 0.272741 35 -0.024858 -0.2436 0.404047 36 -0.068303 -0.6692 0.252477 37 0.015987 0.1566 0.43793 38 0.188156 1.8435 0.034168 39 0.14179 1.3893 0.083985 40 0.01602 0.157 0.4378 41 -0.043958 -0.4307 0.333826 42 -0.172117 -1.6864 0.047483 43 -0.044766 -0.4386 0.330964 44 -0.009146 -0.0896 0.46439 45 0.04002 0.3921 0.347923 46 -0.093122 -0.9124 0.18192 47 -0.110404 -1.0817 0.141041 48 -0.006556 -0.0642 0.474457 Partial Autocorrelation Function Time lag k PACF(k) T-STAT P-value 1 0.2747 2.6915 0.004196 2 -0.054778 -0.5367 0.296355 3 -0.142054 -1.3918 0.083594 4 0.251466 2.4639 0.007762 5 0.043334 0.4246 0.336044 6 -0.00395 -0.0387 0.484606 7 0.166284 1.6292 0.05327 8 -0.055859 -0.5473 0.292719 9 -0.0676 -0.6623 0.254669 10 0.012741 0.1248 0.450459 11 0.020853 0.2043 0.41927 12 -0.092014 -0.9015 0.184776 13 0.048068 0.471 0.319367 14 -0.080476 -0.7885 0.216174 15 -0.081087 -0.7945 0.214435 16 -0.145839 -1.4289 0.078136 17 -0.127024 -1.2446 0.108159 18 0.014728 0.1443 0.442781 19 0.009265 0.0908 0.463927 20 0.04259 0.4173 0.338696 21 -0.051035 -0.5 0.309096 22 -0.008329 -0.0816 0.467565 23 -0.070635 -0.6921 0.245279 24 -0.052564 -0.515 0.303863 25 0.035493 0.3478 0.36439 26 0.106577 1.0442 0.1495 27 -0.049644 -0.4864 0.313893 28 0.071246 0.6981 0.243411 29 -0.026244 -0.2571 0.398813 30 -0.032392 -0.3174 0.375825 31 0.006779 0.0664 0.473589 32 -0.072065 -0.7061 0.240921 33 0.00748 0.0733 0.470864 34 0.080392 0.7877 0.216413 35 -0.11406 -1.1176 0.133274 36 -0.016395 -0.1606 0.436357 37 0.04144 0.406 0.342815 38 0.096178 0.9423 0.17419 39 0.005477 0.0537 0.478657 40 -0.004661 -0.0457 0.481834 41 -0.008658 -0.0848 0.466287 42 -0.215941 -2.1158 0.018475 43 0.047033 0.4608 0.322983 44 -0.036558 -0.3582 0.360494 45 -0.110663 -1.0843 0.140481 46 -0.062313 -0.6105 0.271472 47 -0.00544 -0.0533 0.478801 48 0.036547 0.3581 0.360534

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/19/t1305824510c77l48x89ftafzb/1z58u1305824696.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305824510c77l48x89ftafzb/1z58u1305824696.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305824510c77l48x89ftafzb/232ai1305824696.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305824510c77l48x89ftafzb/232ai1305824696.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305824510c77l48x89ftafzb/3zy961305824696.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305824510c77l48x89ftafzb/3zy961305824696.ps (open in new window)

Parameters (Session):

par1 = Studio 100 PRIJS 2005 ; par2 = Studio 100 PRIJS 2005 ; par3 = Studio 100 PRIJS 2005 ; par4 = 12 ;

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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