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Paper - Werkloosheid 25-50 jaar (leeftijd) Met Autocorrelation

*The author of this computation has been verified*
R Software Module: /rwasp_autocorrelation.wasp (opens new window with default values)
Title produced by software: (Partial) Autocorrelation Function
Date of computation: Thu, 09 Dec 2010 21:14:29 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/09/t1291929152f4i96ivoibzet1e.htm/, Retrieved Thu, 09 Dec 2010 22:12:34 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/09/t1291929152f4i96ivoibzet1e.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
376.974 377.632 378.205 370.861 369.167 371.551 382.842 381.903 384.502 392.058 384.359 388.884 386.586 387.495 385.705 378.67 377.367 376.911 389.827 387.82 387.267 380.575 372.402 376.74 377.795 376.126 370.804 367.98 367.866 366.121 379.421 378.519 372.423 355.072 344.693 342.892 344.178 337.606 327.103 323.953 316.532 306.307 327.225 329.573 313.761 307.836 300.074 304.198 306.122 300.414 292.133 290.616 280.244 285.179 305.486 305.957 293.886 289.441 288.776 299.149 306.532 309.914 313.468 314.901 309.16 316.15 336.544 339.196 326.738 320.838 318.62 331.533 335.378
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ 72.249.76.132


Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.9688078.27750
20.9273357.92320
30.8972797.66640
40.881057.52770
50.87027.4350
60.8472597.2390
70.8037386.86710
80.7490036.39950
90.698425.96730
100.6567985.61170
110.6297335.38040
120.5894655.03642e-06
130.5167754.41531.7e-05
140.4405883.76440.000168
150.3769693.22080.000955
160.3288452.80960.003181
170.2883592.46370.008053
180.2412932.06160.021403
190.177181.51380.067194
200.1052740.89950.185681
210.0429240.36670.357437
22-0.002897-0.02470.490162
23-0.037359-0.31920.375245
24-0.080501-0.68780.246879
25-0.14203-1.21350.114425
26-0.200841-1.7160.045203
27-0.244602-2.08990.020056
28-0.273502-2.33680.011098
29-0.294753-2.51840.006991
30-0.318877-2.72450.004027
31-0.354966-3.03280.001677
32-0.395942-3.38290.000578
33-0.422652-3.61110.000278
34-0.433-3.69960.000208
35-0.437287-3.73620.000184
36-0.443722-3.79120.000153
37-0.461495-3.9439.1e-05
38-0.477586-4.08055.7e-05
39-0.479981-4.1015.3e-05
40-0.471294-4.02676.8e-05
41-0.454475-3.8830.000112
42-0.437497-3.7380.000183
43-0.433174-3.7010.000207
44-0.43209-3.69180.000213
45-0.418948-3.57950.000308
46-0.400655-3.42320.000509
47-0.378608-3.23480.000915
48-0.359209-3.06910.001507
49-0.347158-2.96610.002037
50-0.333834-2.85230.002821
51-0.312083-2.66640.004717
52-0.284201-2.42820.008819
53-0.251311-2.14720.017549
54-0.225486-1.92660.028964
55-0.21033-1.79710.038231
56-0.196782-1.68130.048488
57-0.176219-1.50560.068241
58-0.154241-1.31780.095839
59-0.132071-1.12840.13142
60-0.112988-0.96540.168774


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.9688078.27750
2-0.183201-1.56530.060922
30.1982671.6940.047264
40.1486841.27040.103995
50.0525310.44880.327443
6-0.174186-1.48820.070496
7-0.260876-2.22890.014449
8-0.185858-1.5880.058308
9-0.075975-0.64910.259145
10-0.055518-0.47430.318334
110.1884571.61020.055838
12-0.227769-1.94610.027748
13-0.422388-3.60890.00028
140.0490270.41890.338264
150.0333580.2850.388222
160.0077780.06650.473598
170.0337580.28840.386917
180.0547710.4680.320601
19-0.070608-0.60330.274098
20-0.094514-0.80750.210994
210.0956290.81710.208277
220.0451270.38560.350471
23-0.111654-0.9540.171624
240.0197120.16840.433358
25-0.028657-0.24480.403633
260.0431670.36880.356666
270.0340150.29060.386081
280.0116340.09940.460547
29-0.01555-0.13290.447334
300.0138130.1180.453188
310.0176190.15050.440377
32-0.048819-0.41710.33891
330.0313560.26790.394763
34-0.013251-0.11320.455084
35-0.123084-1.05160.148219
360.1369031.16970.122964
37-0.007224-0.06170.475478
38-0.06084-0.51980.302381
390.0435510.37210.35545
40-0.01599-0.13660.445855
410.0572330.4890.313155
420.06220.53140.298366
43-0.027836-0.23780.40634
440.0086680.07410.470582
45-0.08574-0.73260.233086
46-0.157026-1.34160.091937
47-0.001964-0.01680.49333
48-0.06964-0.5950.276839
490.0269840.23060.409154
50-0.006227-0.05320.478859
510.0356640.30470.380727
520.0484360.41380.340102
530.0222670.19020.424822
54-0.093948-0.80270.21238
550.0640050.54690.293072
56-0.050215-0.4290.334581
57-0.028197-0.24090.405147
58-0.042269-0.36110.359516
59-0.021964-0.18770.425832
60-0.050568-0.43210.333489
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291929152f4i96ivoibzet1e/1zm4k1291929266.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291929152f4i96ivoibzet1e/1zm4k1291929266.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t1291929152f4i96ivoibzet1e/2zm4k1291929266.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291929152f4i96ivoibzet1e/2zm4k1291929266.ps (open in new window)


 
Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
 
Parameters (R input):
par1 = 60 ; 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 (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='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep=''))
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 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')
 





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