R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(3,0,3.21,0,3.37,0,3.51,0,3.75,0,4.11,0,4.25,0,4.25,0,4.5,0,4.7,0,4.75,0,4.75,0,4.75,0,4.75,0,4.75,0,4.75,0,4.58,0,4.5,0,4.5,0,4.49,0,4.03,0,3.75,0,3.39,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,2.85,0,2.75,0,2.75,0,2.55,0,2.5,0,2.5,0,2.1,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2.21,0,2.25,0,2.25,0,2.45,0,2.5,0,2.5,0,2.64,0,2.75,0,2.93,0,3,0,3.17,0,3.25,0,3.39,0,3.5,0,3.5,0,3.65,0,3.75,0,3.75,0,3.9,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4.18,0,4.25,0,4.25,0,3.97,1,3.42,1,2.75,1,2.31,1,2,1,1.66,1,1.31,1,1.09,1,1,1,1,1,1,1,1,1,1,1),dim=c(2,118),dimnames=list(c('Rente','Crisis'),1:118))
> y <- array(NA,dim=c(2,118),dimnames=list(c('Rente','Crisis'),1:118))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Rente Crisis t
1 3.00 0 1
2 3.21 0 2
3 3.37 0 3
4 3.51 0 4
5 3.75 0 5
6 4.11 0 6
7 4.25 0 7
8 4.25 0 8
9 4.50 0 9
10 4.70 0 10
11 4.75 0 11
12 4.75 0 12
13 4.75 0 13
14 4.75 0 14
15 4.75 0 15
16 4.75 0 16
17 4.58 0 17
18 4.50 0 18
19 4.50 0 19
20 4.49 0 20
21 4.03 0 21
22 3.75 0 22
23 3.39 0 23
24 3.25 0 24
25 3.25 0 25
26 3.25 0 26
27 3.25 0 27
28 3.25 0 28
29 3.25 0 29
30 3.25 0 30
31 3.25 0 31
32 3.25 0 32
33 3.25 0 33
34 3.25 0 34
35 3.25 0 35
36 2.85 0 36
37 2.75 0 37
38 2.75 0 38
39 2.55 0 39
40 2.50 0 40
41 2.50 0 41
42 2.10 0 42
43 2.00 0 43
44 2.00 0 44
45 2.00 0 45
46 2.00 0 46
47 2.00 0 47
48 2.00 0 48
49 2.00 0 49
50 2.00 0 50
51 2.00 0 51
52 2.00 0 52
53 2.00 0 53
54 2.00 0 54
55 2.00 0 55
56 2.00 0 56
57 2.00 0 57
58 2.00 0 58
59 2.00 0 59
60 2.00 0 60
61 2.00 0 61
62 2.00 0 62
63 2.00 0 63
64 2.00 0 64
65 2.00 0 65
66 2.00 0 66
67 2.00 0 67
68 2.00 0 68
69 2.00 0 69
70 2.00 0 70
71 2.00 0 71
72 2.21 0 72
73 2.25 0 73
74 2.25 0 74
75 2.45 0 75
76 2.50 0 76
77 2.50 0 77
78 2.64 0 78
79 2.75 0 79
80 2.93 0 80
81 3.00 0 81
82 3.17 0 82
83 3.25 0 83
84 3.39 0 84
85 3.50 0 85
86 3.50 0 86
87 3.65 0 87
88 3.75 0 88
89 3.75 0 89
90 3.90 0 90
91 4.00 0 91
92 4.00 0 92
93 4.00 0 93
94 4.00 0 94
95 4.00 0 95
96 4.00 0 96
97 4.00 0 97
98 4.00 0 98
99 4.00 0 99
100 4.00 0 100
101 4.00 0 101
102 4.00 0 102
103 4.18 0 103
104 4.25 0 104
105 4.25 0 105
106 3.97 1 106
107 3.42 1 107
108 2.75 1 108
109 2.31 1 109
110 2.00 1 110
111 1.66 1 111
112 1.31 1 112
113 1.09 1 113
114 1.00 1 114
115 1.00 1 115
116 1.00 1 116
117 1.00 1 117
118 1.00 1 118
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Crisis t
3.412367 -1.022501 -0.005191
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.1891 -0.9900 -0.0245 0.9463 2.1304
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.412367 0.184609 18.484 < 2e-16 ***
Crisis -1.022501 0.328870 -3.109 0.00237 **
t -0.005191 0.003023 -1.717 0.08863 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9398 on 115 degrees of freedom
Multiple R-squared: 0.1848, Adjusted R-squared: 0.1707
F-statistic: 13.04 on 2 and 115 DF, p-value: 7.88e-06
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 9.713879e-04 1.942776e-03 9.990286e-01
[2,] 7.355153e-05 1.471031e-04 9.999264e-01
[3,] 2.824103e-05 5.648207e-05 9.999718e-01
[4,] 2.940065e-06 5.880129e-06 9.999971e-01
[5,] 2.729793e-07 5.459585e-07 9.999997e-01
[6,] 8.085320e-08 1.617064e-07 9.999999e-01
[7,] 1.473572e-07 2.947144e-07 9.999999e-01
[8,] 3.904278e-07 7.808557e-07 9.999996e-01
[9,] 9.329115e-07 1.865823e-06 9.999991e-01
[10,] 1.942740e-06 3.885480e-06 9.999981e-01
[11,] 3.679537e-06 7.359073e-06 9.999963e-01
[12,] 1.699419e-05 3.398838e-05 9.999830e-01
[13,] 6.323852e-05 1.264770e-04 9.999368e-01
[14,] 1.497119e-04 2.994239e-04 9.998503e-01
[15,] 3.024678e-04 6.049356e-04 9.996975e-01
[16,] 2.058197e-03 4.116394e-03 9.979418e-01
[17,] 1.144756e-02 2.289513e-02 9.885524e-01
[18,] 5.127604e-02 1.025521e-01 9.487240e-01
[19,] 1.209522e-01 2.419043e-01 8.790478e-01
[20,] 1.846310e-01 3.692619e-01 8.153690e-01
[21,] 2.318652e-01 4.637305e-01 7.681348e-01
[22,] 2.636810e-01 5.273620e-01 7.363190e-01
[23,] 2.842492e-01 5.684985e-01 7.157508e-01
[24,] 2.978907e-01 5.957815e-01 7.021093e-01
[25,] 3.083836e-01 6.167671e-01 6.916164e-01
[26,] 3.190013e-01 6.380026e-01 6.809987e-01
[27,] 3.327626e-01 6.655251e-01 6.672374e-01
[28,] 3.527540e-01 7.055081e-01 6.472460e-01
[29,] 3.824996e-01 7.649992e-01 6.175004e-01
[30,] 4.263306e-01 8.526613e-01 5.736694e-01
[31,] 4.627877e-01 9.255755e-01 5.372123e-01
[32,] 4.984920e-01 9.969840e-01 5.015080e-01
[33,] 5.336337e-01 9.327327e-01 4.663663e-01
[34,] 5.656829e-01 8.686341e-01 4.343171e-01
[35,] 5.926231e-01 8.147537e-01 4.073769e-01
[36,] 6.163239e-01 7.673523e-01 3.836761e-01
[37,] 6.402951e-01 7.194098e-01 3.597049e-01
[38,] 6.544127e-01 6.911746e-01 3.455873e-01
[39,] 6.540370e-01 6.919259e-01 3.459630e-01
[40,] 6.428259e-01 7.143482e-01 3.571741e-01
[41,] 6.231530e-01 7.536939e-01 3.768470e-01
[42,] 5.967168e-01 8.065664e-01 4.032832e-01
[43,] 5.648871e-01 8.702258e-01 4.351129e-01
[44,] 5.288886e-01 9.422228e-01 4.711114e-01
[45,] 4.898830e-01 9.797660e-01 5.101170e-01
[46,] 4.489897e-01 8.979794e-01 5.510103e-01
[47,] 4.072715e-01 8.145430e-01 5.927285e-01
[48,] 3.657055e-01 7.314110e-01 6.342945e-01
[49,] 3.251519e-01 6.503038e-01 6.748481e-01
[50,] 2.863303e-01 5.726607e-01 7.136697e-01
[51,] 2.498070e-01 4.996140e-01 7.501930e-01
[52,] 2.159938e-01 4.319876e-01 7.840062e-01
[53,] 1.851574e-01 3.703149e-01 8.148426e-01
[54,] 1.574353e-01 3.148705e-01 8.425647e-01
[55,] 1.328550e-01 2.657100e-01 8.671450e-01
[56,] 1.113554e-01 2.227108e-01 8.886446e-01
[57,] 9.280655e-02 1.856131e-01 9.071934e-01
[58,] 7.702798e-02 1.540560e-01 9.229720e-01
[59,] 6.380565e-02 1.276113e-01 9.361943e-01
[60,] 5.290736e-02 1.058147e-01 9.470926e-01
[61,] 4.409761e-02 8.819521e-02 9.559024e-01
[62,] 3.715318e-02 7.430635e-02 9.628468e-01
[63,] 3.188120e-02 6.376239e-02 9.681188e-01
[64,] 2.814352e-02 5.628703e-02 9.718565e-01
[65,] 2.589601e-02 5.179201e-02 9.741040e-01
[66,] 2.526373e-02 5.052746e-02 9.747363e-01
[67,] 2.668144e-02 5.336288e-02 9.733186e-01
[68,] 2.965880e-02 5.931760e-02 9.703412e-01
[69,] 3.481615e-02 6.963231e-02 9.651838e-01
[70,] 4.400628e-02 8.801256e-02 9.559937e-01
[71,] 5.750030e-02 1.150006e-01 9.424997e-01
[72,] 7.784213e-02 1.556843e-01 9.221579e-01
[73,] 1.089297e-01 2.178594e-01 8.910703e-01
[74,] 1.542750e-01 3.085499e-01 8.457250e-01
[75,] 2.161110e-01 4.322221e-01 7.838890e-01
[76,] 2.943913e-01 5.887825e-01 7.056087e-01
[77,] 3.854991e-01 7.709981e-01 6.145009e-01
[78,] 4.834782e-01 9.669565e-01 5.165218e-01
[79,] 5.796493e-01 8.407013e-01 4.203507e-01
[80,] 6.668408e-01 6.663185e-01 3.331592e-01
[81,] 7.471515e-01 5.056970e-01 2.528485e-01
[82,] 8.113437e-01 3.773126e-01 1.886563e-01
[83,] 8.607347e-01 2.785306e-01 1.392653e-01
[84,] 9.018531e-01 1.962937e-01 9.814685e-02
[85,] 9.291333e-01 1.417333e-01 7.086666e-02
[86,] 9.470423e-01 1.059153e-01 5.295766e-02
[87,] 9.606711e-01 7.865779e-02 3.932890e-02
[88,] 9.713048e-01 5.739049e-02 2.869524e-02
[89,] 9.796275e-01 4.074497e-02 2.037248e-02
[90,] 9.860069e-01 2.798618e-02 1.399309e-02
[91,] 9.906934e-01 1.861325e-02 9.306623e-03
[92,] 9.939423e-01 1.211545e-02 6.057724e-03
[93,] 9.960511e-01 7.897735e-03 3.948867e-03
[94,] 9.973291e-01 5.341750e-03 2.670875e-03
[95,] 9.980443e-01 3.911433e-03 1.955716e-03
[96,] 9.983914e-01 3.217171e-03 1.608585e-03
[97,] 9.984995e-01 3.000988e-03 1.500494e-03
[98,] 9.976501e-01 4.699895e-03 2.349947e-03
[99,] 9.954712e-01 9.057589e-03 4.528794e-03
[100,] 9.908189e-01 1.836230e-02 9.181148e-03
[101,] 9.957908e-01 8.418469e-03 4.209235e-03
[102,] 9.986486e-01 2.702847e-03 1.351423e-03
[103,] 9.990129e-01 1.974271e-03 9.871357e-04
[104,] 9.989963e-01 2.007491e-03 1.003746e-03
[105,] 9.993180e-01 1.364082e-03 6.820409e-04
[106,] 9.997655e-01 4.689568e-04 2.344784e-04
[107,] 9.999211e-01 1.578381e-04 7.891907e-05
> postscript(file="/var/www/html/rcomp/tmp/1pkrs1258737674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2wxx71258737674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3gewl1258737674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/468f31258737674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5eh901258737674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 118
Frequency = 1
1 2 3 4 5 6
-0.407176217 -0.191985100 -0.026793982 0.118397136 0.363588254 0.728779371
7 8 9 10 11 12
0.873970489 0.879161607 1.134352724 1.339543842 1.394734960 1.399926078
13 14 15 16 17 18
1.405117195 1.410308313 1.415499431 1.420690549 1.255881666 1.181072784
19 20 21 22 23 24
1.186263902 1.181455020 0.726646137 0.451837255 0.097028373 -0.037780510
25 26 27 28 29 30
-0.032589392 -0.027398274 -0.022207156 -0.017016039 -0.011824921 -0.006633803
31 32 33 34 35 36
-0.001442685 0.003748432 0.008939550 0.014130668 0.019321786 -0.375487097
37 38 39 40 41 42
-0.470295979 -0.465104861 -0.659913744 -0.704722626 -0.699531508 -1.094340390
43 44 45 46 47 48
-1.189149273 -1.183958155 -1.178767037 -1.173575919 -1.168384802 -1.163193684
49 50 51 52 53 54
-1.158002566 -1.152811448 -1.147620331 -1.142429213 -1.137238095 -1.132046978
55 56 57 58 59 60
-1.126855860 -1.121664742 -1.116473624 -1.111282507 -1.106091389 -1.100900271
61 62 63 64 65 66
-1.095709153 -1.090518036 -1.085326918 -1.080135800 -1.074944682 -1.069753565
67 68 69 70 71 72
-1.064562447 -1.059371329 -1.054180212 -1.048989094 -1.043797976 -0.828606858
73 74 75 76 77 78
-0.783415741 -0.778224623 -0.573033505 -0.517842387 -0.512651270 -0.367460152
79 80 81 82 83 84
-0.252269034 -0.067077916 0.008113201 0.183304319 0.268495437 0.413686555
85 86 87 88 89 90
0.528877672 0.534068790 0.689259908 0.794451025 0.799642143 0.954833261
91 92 93 94 95 96
1.060024379 1.065215496 1.070406614 1.075597732 1.080788850 1.085979967
97 98 99 100 101 102
1.091171085 1.096362203 1.101553321 1.106744438 1.111935556 1.117126674
103 104 105 106 107 108
1.302317791 1.377508909 1.382700027 2.130391755 1.585582873 0.920773991
109 110 111 112 113 114
0.485965108 0.181156226 -0.153652656 -0.498461538 -0.713270421 -0.798079303
115 116 117 118
-0.792888185 -0.787697068 -0.782505950 -0.777314832
> postscript(file="/var/www/html/rcomp/tmp/6fcd61258737674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 118
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.407176217 NA
1 -0.191985100 -0.407176217
2 -0.026793982 -0.191985100
3 0.118397136 -0.026793982
4 0.363588254 0.118397136
5 0.728779371 0.363588254
6 0.873970489 0.728779371
7 0.879161607 0.873970489
8 1.134352724 0.879161607
9 1.339543842 1.134352724
10 1.394734960 1.339543842
11 1.399926078 1.394734960
12 1.405117195 1.399926078
13 1.410308313 1.405117195
14 1.415499431 1.410308313
15 1.420690549 1.415499431
16 1.255881666 1.420690549
17 1.181072784 1.255881666
18 1.186263902 1.181072784
19 1.181455020 1.186263902
20 0.726646137 1.181455020
21 0.451837255 0.726646137
22 0.097028373 0.451837255
23 -0.037780510 0.097028373
24 -0.032589392 -0.037780510
25 -0.027398274 -0.032589392
26 -0.022207156 -0.027398274
27 -0.017016039 -0.022207156
28 -0.011824921 -0.017016039
29 -0.006633803 -0.011824921
30 -0.001442685 -0.006633803
31 0.003748432 -0.001442685
32 0.008939550 0.003748432
33 0.014130668 0.008939550
34 0.019321786 0.014130668
35 -0.375487097 0.019321786
36 -0.470295979 -0.375487097
37 -0.465104861 -0.470295979
38 -0.659913744 -0.465104861
39 -0.704722626 -0.659913744
40 -0.699531508 -0.704722626
41 -1.094340390 -0.699531508
42 -1.189149273 -1.094340390
43 -1.183958155 -1.189149273
44 -1.178767037 -1.183958155
45 -1.173575919 -1.178767037
46 -1.168384802 -1.173575919
47 -1.163193684 -1.168384802
48 -1.158002566 -1.163193684
49 -1.152811448 -1.158002566
50 -1.147620331 -1.152811448
51 -1.142429213 -1.147620331
52 -1.137238095 -1.142429213
53 -1.132046978 -1.137238095
54 -1.126855860 -1.132046978
55 -1.121664742 -1.126855860
56 -1.116473624 -1.121664742
57 -1.111282507 -1.116473624
58 -1.106091389 -1.111282507
59 -1.100900271 -1.106091389
60 -1.095709153 -1.100900271
61 -1.090518036 -1.095709153
62 -1.085326918 -1.090518036
63 -1.080135800 -1.085326918
64 -1.074944682 -1.080135800
65 -1.069753565 -1.074944682
66 -1.064562447 -1.069753565
67 -1.059371329 -1.064562447
68 -1.054180212 -1.059371329
69 -1.048989094 -1.054180212
70 -1.043797976 -1.048989094
71 -0.828606858 -1.043797976
72 -0.783415741 -0.828606858
73 -0.778224623 -0.783415741
74 -0.573033505 -0.778224623
75 -0.517842387 -0.573033505
76 -0.512651270 -0.517842387
77 -0.367460152 -0.512651270
78 -0.252269034 -0.367460152
79 -0.067077916 -0.252269034
80 0.008113201 -0.067077916
81 0.183304319 0.008113201
82 0.268495437 0.183304319
83 0.413686555 0.268495437
84 0.528877672 0.413686555
85 0.534068790 0.528877672
86 0.689259908 0.534068790
87 0.794451025 0.689259908
88 0.799642143 0.794451025
89 0.954833261 0.799642143
90 1.060024379 0.954833261
91 1.065215496 1.060024379
92 1.070406614 1.065215496
93 1.075597732 1.070406614
94 1.080788850 1.075597732
95 1.085979967 1.080788850
96 1.091171085 1.085979967
97 1.096362203 1.091171085
98 1.101553321 1.096362203
99 1.106744438 1.101553321
100 1.111935556 1.106744438
101 1.117126674 1.111935556
102 1.302317791 1.117126674
103 1.377508909 1.302317791
104 1.382700027 1.377508909
105 2.130391755 1.382700027
106 1.585582873 2.130391755
107 0.920773991 1.585582873
108 0.485965108 0.920773991
109 0.181156226 0.485965108
110 -0.153652656 0.181156226
111 -0.498461538 -0.153652656
112 -0.713270421 -0.498461538
113 -0.798079303 -0.713270421
114 -0.792888185 -0.798079303
115 -0.787697068 -0.792888185
116 -0.782505950 -0.787697068
117 -0.777314832 -0.782505950
118 NA -0.777314832
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.191985100 -0.407176217
[2,] -0.026793982 -0.191985100
[3,] 0.118397136 -0.026793982
[4,] 0.363588254 0.118397136
[5,] 0.728779371 0.363588254
[6,] 0.873970489 0.728779371
[7,] 0.879161607 0.873970489
[8,] 1.134352724 0.879161607
[9,] 1.339543842 1.134352724
[10,] 1.394734960 1.339543842
[11,] 1.399926078 1.394734960
[12,] 1.405117195 1.399926078
[13,] 1.410308313 1.405117195
[14,] 1.415499431 1.410308313
[15,] 1.420690549 1.415499431
[16,] 1.255881666 1.420690549
[17,] 1.181072784 1.255881666
[18,] 1.186263902 1.181072784
[19,] 1.181455020 1.186263902
[20,] 0.726646137 1.181455020
[21,] 0.451837255 0.726646137
[22,] 0.097028373 0.451837255
[23,] -0.037780510 0.097028373
[24,] -0.032589392 -0.037780510
[25,] -0.027398274 -0.032589392
[26,] -0.022207156 -0.027398274
[27,] -0.017016039 -0.022207156
[28,] -0.011824921 -0.017016039
[29,] -0.006633803 -0.011824921
[30,] -0.001442685 -0.006633803
[31,] 0.003748432 -0.001442685
[32,] 0.008939550 0.003748432
[33,] 0.014130668 0.008939550
[34,] 0.019321786 0.014130668
[35,] -0.375487097 0.019321786
[36,] -0.470295979 -0.375487097
[37,] -0.465104861 -0.470295979
[38,] -0.659913744 -0.465104861
[39,] -0.704722626 -0.659913744
[40,] -0.699531508 -0.704722626
[41,] -1.094340390 -0.699531508
[42,] -1.189149273 -1.094340390
[43,] -1.183958155 -1.189149273
[44,] -1.178767037 -1.183958155
[45,] -1.173575919 -1.178767037
[46,] -1.168384802 -1.173575919
[47,] -1.163193684 -1.168384802
[48,] -1.158002566 -1.163193684
[49,] -1.152811448 -1.158002566
[50,] -1.147620331 -1.152811448
[51,] -1.142429213 -1.147620331
[52,] -1.137238095 -1.142429213
[53,] -1.132046978 -1.137238095
[54,] -1.126855860 -1.132046978
[55,] -1.121664742 -1.126855860
[56,] -1.116473624 -1.121664742
[57,] -1.111282507 -1.116473624
[58,] -1.106091389 -1.111282507
[59,] -1.100900271 -1.106091389
[60,] -1.095709153 -1.100900271
[61,] -1.090518036 -1.095709153
[62,] -1.085326918 -1.090518036
[63,] -1.080135800 -1.085326918
[64,] -1.074944682 -1.080135800
[65,] -1.069753565 -1.074944682
[66,] -1.064562447 -1.069753565
[67,] -1.059371329 -1.064562447
[68,] -1.054180212 -1.059371329
[69,] -1.048989094 -1.054180212
[70,] -1.043797976 -1.048989094
[71,] -0.828606858 -1.043797976
[72,] -0.783415741 -0.828606858
[73,] -0.778224623 -0.783415741
[74,] -0.573033505 -0.778224623
[75,] -0.517842387 -0.573033505
[76,] -0.512651270 -0.517842387
[77,] -0.367460152 -0.512651270
[78,] -0.252269034 -0.367460152
[79,] -0.067077916 -0.252269034
[80,] 0.008113201 -0.067077916
[81,] 0.183304319 0.008113201
[82,] 0.268495437 0.183304319
[83,] 0.413686555 0.268495437
[84,] 0.528877672 0.413686555
[85,] 0.534068790 0.528877672
[86,] 0.689259908 0.534068790
[87,] 0.794451025 0.689259908
[88,] 0.799642143 0.794451025
[89,] 0.954833261 0.799642143
[90,] 1.060024379 0.954833261
[91,] 1.065215496 1.060024379
[92,] 1.070406614 1.065215496
[93,] 1.075597732 1.070406614
[94,] 1.080788850 1.075597732
[95,] 1.085979967 1.080788850
[96,] 1.091171085 1.085979967
[97,] 1.096362203 1.091171085
[98,] 1.101553321 1.096362203
[99,] 1.106744438 1.101553321
[100,] 1.111935556 1.106744438
[101,] 1.117126674 1.111935556
[102,] 1.302317791 1.117126674
[103,] 1.377508909 1.302317791
[104,] 1.382700027 1.377508909
[105,] 2.130391755 1.382700027
[106,] 1.585582873 2.130391755
[107,] 0.920773991 1.585582873
[108,] 0.485965108 0.920773991
[109,] 0.181156226 0.485965108
[110,] -0.153652656 0.181156226
[111,] -0.498461538 -0.153652656
[112,] -0.713270421 -0.498461538
[113,] -0.798079303 -0.713270421
[114,] -0.792888185 -0.798079303
[115,] -0.787697068 -0.792888185
[116,] -0.782505950 -0.787697068
[117,] -0.777314832 -0.782505950
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.191985100 -0.407176217
2 -0.026793982 -0.191985100
3 0.118397136 -0.026793982
4 0.363588254 0.118397136
5 0.728779371 0.363588254
6 0.873970489 0.728779371
7 0.879161607 0.873970489
8 1.134352724 0.879161607
9 1.339543842 1.134352724
10 1.394734960 1.339543842
11 1.399926078 1.394734960
12 1.405117195 1.399926078
13 1.410308313 1.405117195
14 1.415499431 1.410308313
15 1.420690549 1.415499431
16 1.255881666 1.420690549
17 1.181072784 1.255881666
18 1.186263902 1.181072784
19 1.181455020 1.186263902
20 0.726646137 1.181455020
21 0.451837255 0.726646137
22 0.097028373 0.451837255
23 -0.037780510 0.097028373
24 -0.032589392 -0.037780510
25 -0.027398274 -0.032589392
26 -0.022207156 -0.027398274
27 -0.017016039 -0.022207156
28 -0.011824921 -0.017016039
29 -0.006633803 -0.011824921
30 -0.001442685 -0.006633803
31 0.003748432 -0.001442685
32 0.008939550 0.003748432
33 0.014130668 0.008939550
34 0.019321786 0.014130668
35 -0.375487097 0.019321786
36 -0.470295979 -0.375487097
37 -0.465104861 -0.470295979
38 -0.659913744 -0.465104861
39 -0.704722626 -0.659913744
40 -0.699531508 -0.704722626
41 -1.094340390 -0.699531508
42 -1.189149273 -1.094340390
43 -1.183958155 -1.189149273
44 -1.178767037 -1.183958155
45 -1.173575919 -1.178767037
46 -1.168384802 -1.173575919
47 -1.163193684 -1.168384802
48 -1.158002566 -1.163193684
49 -1.152811448 -1.158002566
50 -1.147620331 -1.152811448
51 -1.142429213 -1.147620331
52 -1.137238095 -1.142429213
53 -1.132046978 -1.137238095
54 -1.126855860 -1.132046978
55 -1.121664742 -1.126855860
56 -1.116473624 -1.121664742
57 -1.111282507 -1.116473624
58 -1.106091389 -1.111282507
59 -1.100900271 -1.106091389
60 -1.095709153 -1.100900271
61 -1.090518036 -1.095709153
62 -1.085326918 -1.090518036
63 -1.080135800 -1.085326918
64 -1.074944682 -1.080135800
65 -1.069753565 -1.074944682
66 -1.064562447 -1.069753565
67 -1.059371329 -1.064562447
68 -1.054180212 -1.059371329
69 -1.048989094 -1.054180212
70 -1.043797976 -1.048989094
71 -0.828606858 -1.043797976
72 -0.783415741 -0.828606858
73 -0.778224623 -0.783415741
74 -0.573033505 -0.778224623
75 -0.517842387 -0.573033505
76 -0.512651270 -0.517842387
77 -0.367460152 -0.512651270
78 -0.252269034 -0.367460152
79 -0.067077916 -0.252269034
80 0.008113201 -0.067077916
81 0.183304319 0.008113201
82 0.268495437 0.183304319
83 0.413686555 0.268495437
84 0.528877672 0.413686555
85 0.534068790 0.528877672
86 0.689259908 0.534068790
87 0.794451025 0.689259908
88 0.799642143 0.794451025
89 0.954833261 0.799642143
90 1.060024379 0.954833261
91 1.065215496 1.060024379
92 1.070406614 1.065215496
93 1.075597732 1.070406614
94 1.080788850 1.075597732
95 1.085979967 1.080788850
96 1.091171085 1.085979967
97 1.096362203 1.091171085
98 1.101553321 1.096362203
99 1.106744438 1.101553321
100 1.111935556 1.106744438
101 1.117126674 1.111935556
102 1.302317791 1.117126674
103 1.377508909 1.302317791
104 1.382700027 1.377508909
105 2.130391755 1.382700027
106 1.585582873 2.130391755
107 0.920773991 1.585582873
108 0.485965108 0.920773991
109 0.181156226 0.485965108
110 -0.153652656 0.181156226
111 -0.498461538 -0.153652656
112 -0.713270421 -0.498461538
113 -0.798079303 -0.713270421
114 -0.792888185 -0.798079303
115 -0.787697068 -0.792888185
116 -0.782505950 -0.787697068
117 -0.777314832 -0.782505950
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/77wvs1258737674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8mfu31258737674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9e98j1258737674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10f24l1258737674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/116ync1258737674.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/1227qu1258737674.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13383c1258737674.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/144l161258737674.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15uofu1258737674.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16404r1258737674.tab")
+ }
>
> system("convert tmp/1pkrs1258737674.ps tmp/1pkrs1258737674.png")
> system("convert tmp/2wxx71258737674.ps tmp/2wxx71258737674.png")
> system("convert tmp/3gewl1258737674.ps tmp/3gewl1258737674.png")
> system("convert tmp/468f31258737674.ps tmp/468f31258737674.png")
> system("convert tmp/5eh901258737674.ps tmp/5eh901258737674.png")
> system("convert tmp/6fcd61258737674.ps tmp/6fcd61258737674.png")
> system("convert tmp/77wvs1258737674.ps tmp/77wvs1258737674.png")
> system("convert tmp/8mfu31258737674.ps tmp/8mfu31258737674.png")
> system("convert tmp/9e98j1258737674.ps tmp/9e98j1258737674.png")
> system("convert tmp/10f24l1258737674.ps tmp/10f24l1258737674.png")
>
>
> proc.time()
user system elapsed
3.231 1.676 6.590