R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(15.27
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+ ,'BUDSISSS')
+ ,1:130))
> y <- array(NA,dim=c(19,130),dimnames=list(c('QBEPIL','PBEPIL','PBELUX','PBABD','PBFRU','PBEPAL','PBESTO','PBEWIT','PBENA','PCHSAN','PWABR','PSOCOLA','PSOBIT','PSPORT','BUDBEER','BUDCHIL','BUDAMB','BUDWATER','BUDSISSS'),1:130))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
QBEPIL PBEPIL PBELUX PBABD PBFRU PBEPAL PBESTO PBEWIT PBENA PCHSAN PWABR
1 15.27 0.21 0.90 0.85 0.92 0.59 0.62 0.98 1.19 0.99 -0.73
2 15.18 0.20 0.90 0.84 0.95 0.58 0.62 0.96 1.17 0.96 -0.72
3 15.13 0.19 0.89 0.86 0.94 0.58 0.61 0.97 1.17 0.95 -0.72
4 15.14 0.20 0.89 0.82 0.95 0.59 0.60 0.98 1.17 0.95 -0.72
5 15.10 0.19 0.89 0.81 0.95 0.58 0.59 0.97 1.18 0.93 -0.72
6 15.17 0.20 0.88 0.84 0.96 0.57 0.61 0.97 1.18 0.94 -0.71
7 15.11 0.19 0.88 0.82 0.95 0.56 0.61 0.97 1.18 0.97 -0.72
8 15.09 0.19 0.88 0.79 0.95 0.57 0.58 0.94 1.18 0.98 -0.72
9 15.10 0.17 0.88 0.81 0.95 0.57 0.59 0.94 1.17 0.97 -0.72
10 15.06 0.17 0.87 0.85 0.94 0.55 0.58 0.96 1.17 0.97 -0.72
11 15.03 0.18 0.88 0.86 0.93 0.56 0.55 0.91 1.17 0.96 -0.71
12 15.03 0.17 0.87 0.80 0.92 0.53 0.55 0.88 1.17 0.96 -0.72
13 15.13 0.17 0.86 0.84 0.92 0.54 0.57 0.92 1.17 0.97 -0.72
14 15.02 0.18 0.88 0.82 0.95 0.57 0.57 0.91 1.18 0.91 -0.71
15 15.01 0.19 0.86 0.83 0.97 0.57 0.55 0.92 1.18 0.93 -0.70
16 15.04 0.19 0.85 0.84 0.97 0.57 0.57 0.93 1.17 0.92 -0.70
17 15.02 0.18 0.85 0.89 0.96 0.57 0.56 0.92 1.18 0.94 -0.70
18 15.00 0.18 0.86 0.90 0.97 0.57 0.58 0.92 1.18 0.96 -0.70
19 15.13 0.19 0.86 0.91 0.98 0.57 0.58 0.91 1.17 0.96 -0.68
20 15.06 0.19 0.88 0.90 0.97 0.58 0.58 0.91 1.17 0.95 -0.68
21 14.90 0.19 0.86 0.89 0.95 0.58 0.58 0.92 1.17 0.88 -0.68
22 14.91 0.18 0.85 0.86 0.94 0.57 0.56 0.90 1.18 0.83 -0.69
23 14.92 0.19 0.86 0.85 0.95 0.56 0.57 0.92 1.18 0.94 -0.70
24 14.97 0.19 0.85 0.77 0.94 0.58 0.57 0.92 1.18 0.94 -0.69
25 14.97 0.19 0.85 0.80 0.95 0.59 0.57 0.92 1.18 1.00 -0.71
26 15.03 0.18 0.85 0.77 0.92 0.57 0.61 0.95 1.19 0.99 -0.71
27 15.01 0.18 0.85 0.79 0.94 0.56 0.62 0.93 1.18 1.01 -0.70
28 15.02 0.18 0.85 0.82 0.97 0.57 0.61 0.93 1.19 1.01 -0.70
29 14.98 0.18 0.86 0.80 1.00 0.54 0.60 0.94 1.20 1.06 -0.69
30 15.03 0.18 0.87 0.84 1.01 0.59 0.60 0.95 1.19 1.07 -0.69
31 14.99 0.20 0.88 0.85 1.01 0.57 0.60 0.95 1.20 1.09 -0.69
32 15.05 0.20 0.87 0.85 0.98 0.59 0.61 0.95 1.20 1.08 -0.69
33 15.04 0.19 0.87 0.83 0.96 0.58 0.61 0.95 1.20 1.07 -0.71
34 15.11 0.23 0.90 0.84 0.98 0.60 0.50 0.95 1.21 1.09 -0.70
35 15.14 0.23 0.88 0.84 0.99 0.59 0.50 0.93 1.21 1.09 -0.70
36 15.06 0.24 0.90 0.84 0.99 0.59 0.51 0.95 1.21 1.08 -0.72
37 15.10 0.25 0.88 0.84 1.00 0.56 0.50 0.93 1.22 1.08 -0.69
38 15.20 0.24 0.84 0.83 1.02 0.55 0.49 0.95 1.20 1.05 -0.70
39 15.13 0.25 0.88 0.84 1.02 0.57 0.50 0.96 1.21 1.07 -0.69
40 15.21 0.25 0.90 0.85 1.00 0.58 0.50 0.97 1.21 1.08 -0.67
41 15.17 0.23 0.90 0.84 0.99 0.57 0.52 0.97 1.21 1.08 -0.68
42 15.18 0.24 0.91 0.84 0.99 0.56 0.51 0.97 1.20 1.07 -0.69
43 15.21 0.23 0.90 0.84 0.99 0.56 0.51 0.97 1.20 1.07 -0.68
44 15.25 0.24 0.90 0.85 0.98 0.58 0.51 0.98 1.21 1.05 -0.67
45 15.18 0.24 0.89 0.86 1.01 0.56 0.49 0.95 1.21 1.06 -0.68
46 15.19 0.24 0.89 0.86 1.02 0.55 0.50 0.96 1.21 1.05 -0.68
47 15.25 0.25 0.90 0.85 1.03 0.57 0.50 0.97 1.22 1.05 -0.67
48 15.21 0.23 0.91 0.82 1.02 0.58 0.52 0.96 1.22 1.04 -0.68
49 15.20 0.24 0.92 0.79 1.03 0.59 0.52 0.97 1.22 1.05 -0.68
50 15.28 0.24 0.92 0.81 1.02 0.60 0.53 0.97 1.22 1.06 -0.68
51 15.41 0.25 0.92 0.84 1.01 0.58 0.53 0.97 1.21 1.07 -0.67
52 15.45 0.25 0.92 0.84 1.00 0.58 0.54 0.97 1.22 1.07 -0.67
53 15.31 0.24 0.93 0.86 1.00 0.58 0.53 0.97 1.23 1.05 -0.68
54 15.19 0.24 0.93 0.86 1.01 0.57 0.52 0.96 1.21 1.05 -0.65
55 15.18 0.22 0.93 0.86 1.01 0.58 0.51 0.96 1.22 1.07 -0.68
56 15.26 0.22 0.93 0.85 1.00 0.59 0.50 0.97 1.23 1.07 -0.66
57 15.24 0.22 0.91 0.84 1.00 0.60 0.51 0.97 1.22 1.07 -0.67
58 15.14 0.22 0.90 0.82 0.98 0.59 0.50 0.96 1.22 1.07 -0.66
59 15.08 0.21 0.89 0.83 0.98 0.58 0.49 0.96 1.22 1.04 -0.67
60 15.12 0.21 0.89 0.83 0.98 0.56 0.48 0.96 1.21 1.04 -0.67
61 15.11 0.21 0.89 0.83 0.99 0.57 0.50 0.95 1.22 1.05 -0.68
62 15.08 0.21 0.88 0.83 0.98 0.57 0.47 0.89 1.22 0.98 -0.68
63 15.06 0.21 0.88 0.86 1.00 0.57 0.47 0.90 1.21 1.01 -0.68
64 15.17 0.21 0.89 0.85 0.99 0.58 0.47 0.93 1.21 1.05 -0.69
65 15.11 0.22 0.88 0.85 0.97 0.58 0.46 0.93 1.21 1.06 -0.70
66 15.03 0.22 0.90 0.83 0.98 0.58 0.49 0.95 1.21 1.06 -0.69
67 15.02 0.23 0.90 0.81 0.99 0.61 0.50 0.92 1.21 1.06 -0.69
68 15.02 0.23 0.90 0.80 0.99 0.65 0.50 0.93 1.21 1.06 -0.68
69 15.04 0.24 0.89 0.82 1.00 0.65 0.49 0.95 1.21 1.05 -0.68
70 15.01 0.23 0.90 0.86 1.01 0.62 0.49 0.94 1.21 1.04 -0.67
71 15.06 0.25 0.90 0.87 1.02 0.57 0.52 0.96 1.21 1.03 -0.63
72 15.09 0.25 0.91 0.88 1.03 0.59 0.51 0.96 1.20 1.04 -0.66
73 15.11 0.25 0.91 0.86 1.01 0.59 0.53 0.97 1.21 1.09 -0.68
74 14.94 0.24 0.89 0.86 0.99 0.59 0.50 0.97 1.21 1.09 -0.69
75 14.94 0.26 0.88 0.86 0.99 0.59 0.51 0.96 1.21 1.08 -0.69
76 14.97 0.26 0.90 0.83 0.99 0.59 0.51 0.98 1.22 1.08 -0.69
77 14.99 0.25 0.89 0.78 1.00 0.59 0.50 0.97 1.22 1.08 -0.69
78 15.06 0.25 0.89 0.80 0.99 0.57 0.51 0.97 1.21 1.08 -0.68
79 15.03 0.24 0.88 0.81 0.99 0.57 0.51 0.98 1.22 1.09 -0.68
80 15.00 0.23 0.89 0.77 0.99 0.57 0.51 0.98 1.22 1.09 -0.69
81 15.01 0.24 0.89 0.80 0.99 0.58 0.50 0.97 1.22 1.09 -0.68
82 15.02 0.24 0.87 0.82 1.00 0.57 0.49 0.97 1.22 1.10 -0.66
83 15.03 0.24 0.87 0.81 0.99 0.59 0.52 0.98 1.22 1.10 -0.66
84 15.08 0.24 0.88 0.81 0.98 0.59 0.51 0.98 1.22 1.09 -0.66
85 15.13 0.26 0.88 0.82 0.99 0.59 0.52 0.98 1.22 1.07 -0.66
86 15.15 0.25 0.86 0.82 0.98 0.58 0.51 0.98 1.22 1.07 -0.70
87 15.14 0.26 0.87 0.84 0.99 0.60 0.51 0.98 1.24 1.10 -0.71
88 15.10 0.26 0.86 0.85 0.98 0.59 0.51 0.98 1.26 1.10 -0.71
89 15.12 0.26 0.87 0.83 0.99 0.58 0.51 0.95 1.25 1.07 -0.72
90 15.23 0.26 0.86 0.83 0.99 0.58 0.51 0.98 1.25 1.07 -0.72
91 15.24 0.26 0.87 0.79 0.98 0.58 0.51 0.98 1.25 1.09 -0.70
92 15.19 0.25 0.98 0.76 0.98 0.58 0.52 0.97 1.25 1.10 -0.67
93 15.21 0.25 0.91 0.76 0.97 0.59 0.50 0.95 1.24 1.09 -0.69
94 15.33 0.26 0.96 0.75 0.97 0.62 0.54 0.97 1.24 1.10 -0.69
95 15.21 0.26 0.97 0.75 0.95 0.66 0.55 0.97 1.24 1.10 -0.69
96 15.19 0.27 0.98 0.77 0.94 0.59 0.54 0.99 1.24 1.11 -0.69
97 15.32 0.27 1.00 0.79 0.97 0.53 0.55 0.97 1.24 1.11 -0.71
98 15.51 0.29 1.00 0.79 0.99 0.55 0.56 0.97 1.24 1.10 -0.71
99 15.34 0.27 0.91 0.82 1.00 0.55 0.55 0.98 1.26 1.06 -0.72
100 15.23 0.26 0.88 0.84 1.00 0.55 0.54 0.96 1.24 1.07 -0.71
101 15.40 0.27 0.90 0.84 1.00 0.57 0.56 0.95 1.24 1.09 -0.73
102 15.23 0.27 0.93 0.85 1.00 0.56 0.54 0.95 1.24 1.08 -0.72
103 15.30 0.27 0.94 0.84 1.00 0.58 0.54 0.97 1.24 1.08 -0.71
104 15.25 0.26 0.92 0.84 1.00 0.58 0.54 0.96 1.24 1.08 -0.71
105 15.22 0.26 0.92 0.85 0.99 0.60 0.54 0.96 1.24 1.08 -0.72
106 15.24 0.26 0.92 0.84 1.00 0.60 0.53 0.95 1.23 1.08 -0.73
107 15.17 0.26 0.92 0.78 0.99 0.61 0.51 0.97 1.25 1.09 -0.73
108 15.31 0.26 0.92 0.76 1.00 0.62 0.53 0.97 1.25 1.09 -0.73
109 15.27 0.26 0.90 0.77 0.99 0.62 0.53 0.97 1.25 1.09 -0.73
110 15.16 0.24 0.90 0.81 0.98 0.61 0.52 0.96 1.25 1.08 -0.72
111 15.18 0.23 0.90 0.83 0.98 0.63 0.54 0.95 1.26 1.10 -0.73
112 15.15 0.22 0.89 0.83 0.99 0.63 0.53 0.98 1.25 1.10 -0.74
113 15.11 0.23 0.89 0.83 0.99 0.65 0.55 0.98 1.26 1.09 -0.70
114 15.15 0.24 0.87 0.84 1.00 0.65 0.54 0.97 1.27 1.10 -0.73
115 15.11 0.23 0.87 0.84 0.99 0.66 0.55 0.97 1.26 1.09 -0.73
116 15.20 0.23 0.87 0.85 1.00 0.64 0.56 0.97 1.24 1.10 -0.73
117 15.10 0.23 0.88 0.84 1.00 0.65 0.54 0.95 1.25 1.09 -0.75
118 15.09 0.23 0.86 0.77 1.00 0.64 0.54 0.86 1.26 1.09 -0.74
119 15.07 0.23 0.87 0.63 1.01 0.65 0.55 0.97 1.26 1.08 -0.74
120 15.00 0.23 0.88 0.70 1.04 0.65 0.55 0.97 1.26 1.08 -0.73
121 15.06 0.23 0.90 0.73 1.01 0.65 0.54 0.97 1.27 1.08 -0.73
122 15.03 0.24 0.91 0.80 1.02 0.67 0.55 0.99 1.27 1.10 -0.73
123 15.06 0.25 0.92 0.88 1.02 0.67 0.55 0.99 1.27 1.12 -0.72
124 15.18 0.26 0.93 0.90 1.03 0.68 0.58 0.99 1.27 1.13 -0.70
125 15.13 0.26 0.93 0.89 1.02 0.68 0.57 0.99 1.28 1.12 -0.71
126 14.99 0.23 0.92 0.87 1.01 0.66 0.54 0.99 1.27 1.11 -0.71
127 14.99 0.22 0.89 0.87 1.01 0.66 0.55 0.99 1.26 1.11 -0.65
128 15.03 0.23 0.93 0.87 1.00 0.65 0.54 0.99 1.26 1.10 -0.65
129 15.03 0.22 0.92 0.86 1.01 0.66 0.54 0.98 1.27 1.13 -0.66
130 15.05 0.21 0.91 0.84 1.00 0.65 0.54 0.98 1.27 1.14 -0.62
PSOCOLA PSOBIT PSPORT BUDBEER BUDCHIL BUDAMB BUDWATER BUDSISSS
1 -0.05 0.17 0.57 16.00 13.09 14.63 15.66 16.12
2 -0.03 0.17 0.59 15.92 13.08 14.52 15.52 16.24
3 -0.03 0.19 0.57 15.86 13.13 14.65 15.54 16.06
4 -0.05 0.17 0.57 15.87 13.14 14.64 15.51 15.91
5 -0.04 0.17 0.59 15.82 13.06 14.56 15.45 15.87
6 -0.04 0.17 0.60 15.89 13.05 14.54 15.41 15.85
7 -0.06 0.16 0.60 15.83 13.12 14.53 15.40 15.92
8 -0.04 0.17 0.60 15.83 13.12 14.57 15.45 15.95
9 -0.03 0.16 0.58 15.80 13.03 14.51 15.38 15.95
10 -0.03 0.17 0.58 15.79 13.06 14.51 15.36 15.91
11 -0.04 0.19 0.59 15.79 13.02 14.49 15.32 15.92
12 -0.05 0.16 0.57 15.81 13.02 14.55 15.35 15.90
13 -0.04 0.19 0.59 15.88 12.96 14.60 15.41 15.90
14 -0.04 0.19 0.60 15.78 13.04 14.50 15.37 15.88
15 -0.03 0.20 0.59 15.78 13.03 14.52 15.38 15.84
16 -0.04 0.17 0.59 15.80 13.02 14.57 15.40 15.85
17 -0.06 0.20 0.58 15.82 12.98 14.61 15.36 15.95
18 -0.05 0.22 0.59 15.82 13.04 14.59 15.34 15.89
19 -0.06 0.21 0.60 16.04 12.95 14.86 15.47 16.07
20 -0.07 0.20 0.59 15.91 12.98 14.71 15.34 15.99
21 -0.02 0.21 0.60 15.69 13.11 14.49 15.28 15.85
22 -0.07 0.19 0.55 15.66 13.07 14.58 15.37 15.94
23 -0.07 0.20 0.57 15.67 13.02 14.54 15.37 15.91
24 -0.04 0.21 0.58 15.73 13.02 14.58 15.40 15.88
25 -0.03 0.17 0.56 15.72 13.07 14.60 15.38 15.95
26 -0.03 0.19 0.56 15.78 13.04 14.63 15.40 15.95
27 -0.06 0.20 0.54 15.75 13.14 14.58 15.37 15.94
28 -0.05 0.20 0.56 15.77 13.15 14.58 15.39 15.89
29 -0.04 0.20 0.57 15.73 13.31 14.62 15.43 15.96
30 -0.02 0.20 0.57 15.77 13.37 14.60 15.44 15.98
31 -0.02 0.20 0.57 15.74 13.33 14.61 15.44 15.92
32 -0.03 0.18 0.55 15.80 13.32 14.63 15.48 15.94
33 -0.02 0.19 0.58 15.78 13.82 14.70 15.52 16.00
34 -0.02 0.18 0.54 15.89 13.53 14.66 15.48 16.08
35 -0.01 0.21 0.57 15.93 13.49 14.58 15.43 16.02
36 -0.01 0.22 0.50 15.83 13.47 14.74 15.59 15.97
37 -0.03 0.21 0.53 15.86 13.81 14.61 15.49 16.02
38 -0.02 0.21 0.55 15.98 13.66 14.65 15.48 16.02
39 -0.03 0.21 0.58 15.92 13.42 14.67 15.54 16.03
40 -0.03 0.21 0.58 15.96 13.44 14.64 15.48 16.15
41 -0.03 0.21 0.58 15.94 13.55 14.63 15.49 16.01
42 -0.02 0.21 0.59 15.96 13.49 14.79 15.70 16.13
43 -0.03 0.20 0.54 15.97 13.44 14.88 15.81 16.12
44 -0.03 0.19 0.59 16.03 13.48 14.70 15.65 16.14
45 -0.02 0.24 0.59 15.94 13.60 14.70 15.62 16.23
46 -0.02 0.20 0.58 15.95 13.38 14.68 15.64 16.15
47 -0.06 0.20 0.57 16.02 13.20 14.54 15.49 16.06
48 -0.03 0.17 0.55 15.96 13.29 14.69 15.68 16.04
49 -0.03 0.17 0.56 15.96 13.11 14.71 15.75 16.04
50 -0.03 0.19 0.56 16.04 13.26 14.53 15.52 16.21
51 -0.03 0.22 0.56 16.17 13.21 14.74 15.81 16.24
52 -0.02 0.22 0.58 16.20 13.09 14.88 16.08 16.12
53 -0.02 0.20 0.57 16.06 13.24 14.65 15.72 16.29
54 -0.02 0.20 0.57 15.96 13.23 14.59 15.53 16.11
55 -0.04 0.16 0.56 15.92 13.45 14.70 15.51 16.17
56 -0.02 0.17 0.57 15.98 13.45 14.68 15.49 16.12
57 0.00 0.18 0.51 15.97 13.33 14.61 15.45 16.06
58 0.01 0.18 0.56 15.88 13.31 14.67 15.57 16.02
59 0.02 0.18 0.59 15.83 13.33 14.63 15.51 16.02
60 0.01 0.17 0.59 15.87 13.27 14.61 15.49 16.08
61 0.01 0.16 0.61 15.85 13.33 14.54 15.40 16.00
62 0.02 0.19 0.61 15.82 13.31 14.57 15.37 15.98
63 0.01 0.20 0.62 15.84 13.32 14.50 15.32 15.99
64 0.01 0.19 0.61 15.95 13.29 14.58 15.35 16.03
65 0.01 0.19 0.60 15.88 13.28 14.63 15.41 16.06
66 0.02 0.21 0.61 15.83 13.33 14.53 15.35 15.96
67 0.02 0.20 0.61 15.82 13.32 14.54 15.36 15.96
68 0.02 0.19 0.60 15.83 13.34 14.56 15.40 16.01
69 0.01 0.19 0.60 15.88 13.27 14.58 15.39 15.99
70 0.01 0.19 0.61 15.86 13.32 14.58 15.36 15.98
71 -0.01 0.20 0.62 15.96 13.34 14.85 15.50 16.20
72 0.00 0.21 0.62 16.01 13.26 14.71 15.29 16.10
73 0.01 0.21 0.62 15.95 13.30 14.59 15.25 15.90
74 -0.01 0.19 0.61 15.75 13.39 14.61 15.44 15.98
75 0.00 0.19 0.61 15.75 13.41 14.58 15.40 15.96
76 0.01 0.20 0.60 15.78 13.41 14.59 15.39 15.96
77 0.01 0.19 0.61 15.78 13.50 14.62 15.41 15.99
78 0.01 0.19 0.62 15.85 13.46 14.66 15.49 16.02
79 0.01 0.18 0.61 15.82 13.44 14.60 15.43 15.99
80 0.03 0.19 0.61 15.80 13.36 14.54 15.41 15.99
81 0.02 0.19 0.59 15.79 13.45 14.60 15.41 16.07
82 0.02 0.20 0.60 15.80 13.47 14.67 15.49 16.06
83 0.03 0.20 0.59 15.81 13.49 14.63 15.45 16.02
84 0.03 0.19 0.59 15.85 13.48 14.62 15.49 16.04
85 0.02 0.20 0.57 15.93 13.44 14.59 15.45 16.12
86 0.02 0.19 0.55 15.91 13.38 14.65 15.56 16.10
87 0.02 0.21 0.57 15.92 13.40 14.62 15.46 16.08
88 0.02 0.20 0.48 15.89 13.46 14.59 15.48 16.23
89 0.03 0.22 0.55 15.89 13.52 14.68 15.51 16.08
90 0.02 0.21 0.54 16.00 13.62 14.74 15.62 16.11
91 0.02 0.23 0.58 16.02 13.61 14.70 15.58 16.14
92 0.02 0.21 0.56 15.98 13.54 14.68 15.57 16.07
93 0.03 0.21 0.57 15.98 13.47 14.63 15.54 16.09
94 0.02 0.19 0.53 16.09 13.50 14.68 15.61 16.33
95 0.02 0.18 0.53 15.98 13.92 14.75 15.63 16.39
96 0.02 0.18 0.55 15.98 13.75 14.76 15.73 16.22
97 0.02 0.21 0.55 16.09 13.64 14.67 15.57 16.24
98 0.02 0.21 0.55 16.26 13.57 14.62 15.48 16.26
99 0.00 0.20 0.58 16.10 13.61 14.65 15.56 16.17
100 0.03 0.21 0.61 16.02 13.50 14.55 15.48 16.34
101 0.04 0.22 0.60 16.18 13.62 14.50 15.42 16.38
102 0.03 0.19 0.60 16.03 13.79 14.56 15.53 16.18
103 0.02 0.19 0.60 16.08 13.51 14.73 15.73 16.45
104 0.04 0.17 0.60 16.04 13.44 14.77 15.85 16.59
105 0.04 0.27 0.60 15.99 13.37 14.65 15.68 16.31
106 0.03 0.20 0.60 16.02 13.43 14.70 15.52 16.18
107 -0.01 0.21 0.60 15.97 13.62 14.57 15.48 16.19
108 0.03 0.21 0.62 16.09 13.54 14.76 15.63 16.17
109 0.07 0.21 0.64 16.04 13.62 14.71 15.67 16.13
110 0.09 0.21 0.67 15.92 13.63 14.59 15.51 16.26
111 0.08 0.22 0.68 15.91 13.70 14.54 15.44 16.18
112 0.05 0.22 0.67 15.91 13.62 14.60 15.47 16.17
113 0.06 0.25 0.68 15.87 13.61 14.61 15.51 16.06
114 0.04 0.25 0.67 15.92 13.58 14.58 15.44 16.10
115 0.06 0.23 0.67 15.92 13.53 14.54 15.42 16.02
116 0.06 0.20 0.67 16.00 13.52 14.63 15.49 16.08
117 0.07 0.21 0.67 15.90 13.52 14.60 15.43 16.12
118 0.08 0.22 0.67 15.90 13.63 14.50 15.33 16.04
119 0.07 0.22 0.65 15.91 13.55 14.52 15.35 16.03
120 0.05 0.22 0.64 15.82 13.57 14.52 15.35 16.08
121 0.04 0.24 0.66 15.90 13.57 14.59 15.40 16.08
122 0.04 0.23 0.65 15.88 13.56 14.72 15.41 16.04
123 0.04 0.23 0.66 15.96 13.59 14.71 15.44 16.25
124 0.04 0.22 0.64 16.13 13.56 14.74 15.33 16.12
125 0.06 0.22 0.65 16.03 13.57 14.71 15.39 16.00
126 0.08 0.22 0.64 15.77 13.65 14.65 15.53 16.08
127 0.08 0.22 0.64 15.76 13.66 14.62 15.54 16.04
128 0.08 0.21 0.64 15.79 13.64 14.62 15.52 16.04
129 0.07 0.20 0.63 15.78 13.68 14.63 15.49 16.08
130 0.06 0.21 0.63 15.84 13.67 14.67 15.56 16.13
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) PBEPIL PBELUX PBABD PBFRU PBEPAL
0.13093 -0.96539 0.07268 0.02492 -0.17543 -0.32265
PBESTO PBEWIT PBENA PCHSAN PWABR PSOCOLA
-0.12653 0.28370 0.11537 -0.01276 -0.16193 0.11014
PSOBIT PSPORT BUDBEER BUDCHIL BUDAMB BUDWATER
-0.16576 -0.27635 0.99401 0.05641 -0.33264 0.26556
BUDSISSS
-0.03513
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.050319 -0.012627 0.000895 0.011565 0.052384
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.13093 0.56723 0.231 0.81788
PBEPIL -0.96539 0.16914 -5.707 9.67e-08 ***
PBELUX 0.07268 0.10123 0.718 0.47430
PBABD 0.02492 0.05706 0.437 0.66318
PBFRU -0.17543 0.12798 -1.371 0.17323
PBEPAL -0.32265 0.09824 -3.284 0.00137 **
PBESTO -0.12653 0.08069 -1.568 0.11972
PBEWIT 0.28370 0.12103 2.344 0.02085 *
PBENA 0.11537 0.19724 0.585 0.55979
PCHSAN -0.01276 0.07910 -0.161 0.87214
PWABR -0.16193 0.11450 -1.414 0.16009
PSOCOLA 0.11014 0.13205 0.834 0.40603
PSOBIT -0.16576 0.13327 -1.244 0.21620
PSPORT -0.27635 0.08472 -3.262 0.00147 **
BUDBEER 0.99401 0.03285 30.261 < 2e-16 ***
BUDCHIL 0.05641 0.01875 3.008 0.00325 **
BUDAMB -0.33264 0.03775 -8.812 1.91e-14 ***
BUDWATER 0.26556 0.02669 9.951 < 2e-16 ***
BUDSISSS -0.03513 0.02713 -1.295 0.19810
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.02222 on 111 degrees of freedom
Multiple R-squared: 0.967, Adjusted R-squared: 0.9617
F-statistic: 180.9 on 18 and 111 DF, p-value: < 2.2e-16
> 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,] 0.5611196663 0.8777606673 0.4388803
[2,] 0.4679082017 0.9358164034 0.5320918
[3,] 0.3859748579 0.7719497158 0.6140251
[4,] 0.2651491824 0.5302983647 0.7348508
[5,] 0.1994849855 0.3989699711 0.8005150
[6,] 0.1472491647 0.2944983294 0.8527508
[7,] 0.1029096649 0.2058193297 0.8970903
[8,] 0.0838236136 0.1676472272 0.9161764
[9,] 0.0538384503 0.1076769007 0.9461615
[10,] 0.0333688412 0.0667376824 0.9666312
[11,] 0.0224272554 0.0448545109 0.9775727
[12,] 0.0178106280 0.0356212559 0.9821894
[13,] 0.0123341206 0.0246682411 0.9876659
[14,] 0.0111551196 0.0223102393 0.9888449
[15,] 0.0061316090 0.0122632180 0.9938684
[16,] 0.0043129789 0.0086259578 0.9956870
[17,] 0.0024140290 0.0048280580 0.9975860
[18,] 0.0012811654 0.0025623308 0.9987188
[19,] 0.0080820250 0.0161640499 0.9919180
[20,] 0.0076952032 0.0153904064 0.9923048
[21,] 0.0047095043 0.0094190085 0.9952905
[22,] 0.0029249653 0.0058499307 0.9970750
[23,] 0.0034845607 0.0069691213 0.9965154
[24,] 0.0060446106 0.0120892212 0.9939554
[25,] 0.0038290078 0.0076580157 0.9961710
[26,] 0.0024868294 0.0049736589 0.9975132
[27,] 0.0014607953 0.0029215905 0.9985392
[28,] 0.0010230436 0.0020460872 0.9989770
[29,] 0.0006308118 0.0012616237 0.9993692
[30,] 0.0007454294 0.0014908588 0.9992546
[31,] 0.0008325578 0.0016651155 0.9991674
[32,] 0.0005144817 0.0010289634 0.9994855
[33,] 0.0002865930 0.0005731861 0.9997134
[34,] 0.0011498984 0.0022997968 0.9988501
[35,] 0.0082897822 0.0165795645 0.9917102
[36,] 0.0081325775 0.0162651551 0.9918674
[37,] 0.0060742427 0.0121484854 0.9939258
[38,] 0.0050264431 0.0100528861 0.9949736
[39,] 0.0036866431 0.0073732861 0.9963134
[40,] 0.0031638843 0.0063277685 0.9968361
[41,] 0.0185451958 0.0370903917 0.9814548
[42,] 0.0129808500 0.0259617000 0.9870192
[43,] 0.0094266822 0.0188533643 0.9905733
[44,] 0.0083763431 0.0167526863 0.9916237
[45,] 0.0200067565 0.0400135130 0.9799932
[46,] 0.0146288101 0.0292576202 0.9853712
[47,] 0.0122579114 0.0245158229 0.9877421
[48,] 0.0124426085 0.0248852170 0.9875574
[49,] 0.0202547655 0.0405095310 0.9797452
[50,] 0.0226646542 0.0453293083 0.9773353
[51,] 0.0376529276 0.0753058551 0.9623471
[52,] 0.0314417938 0.0628835876 0.9685582
[53,] 0.0294392459 0.0588784917 0.9705608
[54,] 0.0214027462 0.0428054924 0.9785973
[55,] 0.0157530886 0.0315061772 0.9842469
[56,] 0.0122757395 0.0245514790 0.9877243
[57,] 0.0085894338 0.0171788676 0.9914106
[58,] 0.0071422695 0.0142845391 0.9928577
[59,] 0.0202958041 0.0405916083 0.9797042
[60,] 0.0139334296 0.0278668591 0.9860666
[61,] 0.0100799792 0.0201599584 0.9899200
[62,] 0.0074590624 0.0149181249 0.9925409
[63,] 0.0048237264 0.0096474529 0.9951763
[64,] 0.0032457139 0.0064914278 0.9967543
[65,] 0.0020126169 0.0040252337 0.9979874
[66,] 0.0024158379 0.0048316759 0.9975842
[67,] 0.0038398878 0.0076797757 0.9961601
[68,] 0.0036149138 0.0072298276 0.9963851
[69,] 0.0026858096 0.0053716192 0.9973142
[70,] 0.0016139151 0.0032278302 0.9983861
[71,] 0.0037639820 0.0075279641 0.9962360
[72,] 0.0040721083 0.0081442166 0.9959279
[73,] 0.0025097177 0.0050194354 0.9974903
[74,] 0.0028173070 0.0056346140 0.9971827
[75,] 0.0026750938 0.0053501877 0.9973249
[76,] 0.0073745788 0.0147491577 0.9926254
[77,] 0.0154639744 0.0309279487 0.9845360
[78,] 0.0126088656 0.0252177312 0.9873911
[79,] 0.0101122142 0.0202244284 0.9898878
[80,] 0.0438699611 0.0877399222 0.9561300
[81,] 0.1813183846 0.3626367692 0.8186816
[82,] 0.1246912979 0.2493825958 0.8753087
[83,] 0.0828376407 0.1656752813 0.9171624
[84,] 0.0615314513 0.1230629025 0.9384685
[85,] 0.0429466335 0.0858932670 0.9570534
[86,] 0.3860755759 0.7721511519 0.6139244
[87,] 0.2589989349 0.5179978698 0.7410011
> postscript(file="/var/wessaorg/rcomp/tmp/1gwtr1333541230.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/2l0ea1333541230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/32v641333541230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4hiaw1333541230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/55pqc1333541230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 130
Frequency = 1
1 2 3 4 5
-0.0126400795 -0.0125780798 0.0082299333 0.0175137533 0.0116009215
6 7 8 9 10
0.0310530210 0.0137058691 0.0030589552 0.0207858155 -0.0208192680
11 12 13 14 15
-0.0150311776 -0.0449945416 -0.0068276592 -0.0163416644 -0.0145190143
16 17 18 19 20
0.0051630660 -0.0125888207 -0.0308813214 -0.0322776257 0.0038884757
21 22 23 24 25
-0.0135497984 0.0126725706 0.0114571913 0.0162566429 0.0263011826
26 27 28 29 30
0.0109824197 0.0153316553 0.0075680675 -0.0025357623 0.0081183324
31 32 33 34 35
0.0123939120 0.0053642443 -0.0085985610 -0.0112159390 -0.0163443702
36 37 38 39 40
-0.0040995977 -0.0078981690 -0.0111813038 0.0045588112 0.0523844833
41 42 43 44 45
-0.0079682768 -0.0072893843 -0.0057155494 -0.0206593051 0.0141239967
46 47 48 49 50
-0.0007463944 0.0065828816 -0.0081136499 -0.0051873949 -0.0003121740
51 52 53 54 55
0.0057551488 -0.0038606632 -0.0093450779 0.0020597576 0.0331267001
56 57 58 59 60
0.0523101396 0.0228515594 0.0105236813 -0.0067572186 -0.0094879583
61 62 63 64 65
0.0063853680 0.0397540794 -0.0033629466 0.0059914296 0.0192402357
66 67 68 69 70
-0.0292055660 0.0016743094 -0.0040904833 -0.0155655838 -0.0331006160
71 72 73 74 75
-0.0074905606 -0.0140482293 0.0188431610 -0.0194244301 0.0022958988
76 77 78 79 80
-0.0012833625 0.0161128010 0.0099989916 -0.0115918121 -0.0433949879
81 82 83 84 85
0.0051359046 0.0117614174 0.0086466333 0.0006724358 -0.0040836233
86 87 88 89 90
0.0011170200 0.0178571664 -0.0390672721 0.0217699459 -0.0025149551
91 92 93 94 95
0.0029178878 -0.0264845075 -0.0013463290 0.0162901710 0.0103008668
96 97 98 99 100
-0.0465076873 -0.0137645671 0.0495800289 0.0104744828 -0.0127127600
101 102 103 104 105
0.0070135855 -0.0503187711 0.0017185042 -0.0290974722 0.0093492825
106 107 108 109 110
0.0433078713 -0.0213323702 0.0356952361 0.0127948307 0.0125170330
111 112 113 114 115
0.0423283796 0.0116073883 0.0312398849 0.0409862326 -0.0184065674
116 117 118 119 120
-0.0001273404 0.0085618950 0.0093648143 -0.0418244511 -0.0178845750
121 122 123 124 125
-0.0273381407 0.0091045739 -0.0344531142 -0.0337314221 -0.0201332003
126 127 128 129 130
-0.0042094976 -0.0042122213 0.0111223656 0.0249346931 -0.0217147711
> postscript(file="/var/wessaorg/rcomp/tmp/6lm9l1333541230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 130
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0126400795 NA
1 -0.0125780798 -0.0126400795
2 0.0082299333 -0.0125780798
3 0.0175137533 0.0082299333
4 0.0116009215 0.0175137533
5 0.0310530210 0.0116009215
6 0.0137058691 0.0310530210
7 0.0030589552 0.0137058691
8 0.0207858155 0.0030589552
9 -0.0208192680 0.0207858155
10 -0.0150311776 -0.0208192680
11 -0.0449945416 -0.0150311776
12 -0.0068276592 -0.0449945416
13 -0.0163416644 -0.0068276592
14 -0.0145190143 -0.0163416644
15 0.0051630660 -0.0145190143
16 -0.0125888207 0.0051630660
17 -0.0308813214 -0.0125888207
18 -0.0322776257 -0.0308813214
19 0.0038884757 -0.0322776257
20 -0.0135497984 0.0038884757
21 0.0126725706 -0.0135497984
22 0.0114571913 0.0126725706
23 0.0162566429 0.0114571913
24 0.0263011826 0.0162566429
25 0.0109824197 0.0263011826
26 0.0153316553 0.0109824197
27 0.0075680675 0.0153316553
28 -0.0025357623 0.0075680675
29 0.0081183324 -0.0025357623
30 0.0123939120 0.0081183324
31 0.0053642443 0.0123939120
32 -0.0085985610 0.0053642443
33 -0.0112159390 -0.0085985610
34 -0.0163443702 -0.0112159390
35 -0.0040995977 -0.0163443702
36 -0.0078981690 -0.0040995977
37 -0.0111813038 -0.0078981690
38 0.0045588112 -0.0111813038
39 0.0523844833 0.0045588112
40 -0.0079682768 0.0523844833
41 -0.0072893843 -0.0079682768
42 -0.0057155494 -0.0072893843
43 -0.0206593051 -0.0057155494
44 0.0141239967 -0.0206593051
45 -0.0007463944 0.0141239967
46 0.0065828816 -0.0007463944
47 -0.0081136499 0.0065828816
48 -0.0051873949 -0.0081136499
49 -0.0003121740 -0.0051873949
50 0.0057551488 -0.0003121740
51 -0.0038606632 0.0057551488
52 -0.0093450779 -0.0038606632
53 0.0020597576 -0.0093450779
54 0.0331267001 0.0020597576
55 0.0523101396 0.0331267001
56 0.0228515594 0.0523101396
57 0.0105236813 0.0228515594
58 -0.0067572186 0.0105236813
59 -0.0094879583 -0.0067572186
60 0.0063853680 -0.0094879583
61 0.0397540794 0.0063853680
62 -0.0033629466 0.0397540794
63 0.0059914296 -0.0033629466
64 0.0192402357 0.0059914296
65 -0.0292055660 0.0192402357
66 0.0016743094 -0.0292055660
67 -0.0040904833 0.0016743094
68 -0.0155655838 -0.0040904833
69 -0.0331006160 -0.0155655838
70 -0.0074905606 -0.0331006160
71 -0.0140482293 -0.0074905606
72 0.0188431610 -0.0140482293
73 -0.0194244301 0.0188431610
74 0.0022958988 -0.0194244301
75 -0.0012833625 0.0022958988
76 0.0161128010 -0.0012833625
77 0.0099989916 0.0161128010
78 -0.0115918121 0.0099989916
79 -0.0433949879 -0.0115918121
80 0.0051359046 -0.0433949879
81 0.0117614174 0.0051359046
82 0.0086466333 0.0117614174
83 0.0006724358 0.0086466333
84 -0.0040836233 0.0006724358
85 0.0011170200 -0.0040836233
86 0.0178571664 0.0011170200
87 -0.0390672721 0.0178571664
88 0.0217699459 -0.0390672721
89 -0.0025149551 0.0217699459
90 0.0029178878 -0.0025149551
91 -0.0264845075 0.0029178878
92 -0.0013463290 -0.0264845075
93 0.0162901710 -0.0013463290
94 0.0103008668 0.0162901710
95 -0.0465076873 0.0103008668
96 -0.0137645671 -0.0465076873
97 0.0495800289 -0.0137645671
98 0.0104744828 0.0495800289
99 -0.0127127600 0.0104744828
100 0.0070135855 -0.0127127600
101 -0.0503187711 0.0070135855
102 0.0017185042 -0.0503187711
103 -0.0290974722 0.0017185042
104 0.0093492825 -0.0290974722
105 0.0433078713 0.0093492825
106 -0.0213323702 0.0433078713
107 0.0356952361 -0.0213323702
108 0.0127948307 0.0356952361
109 0.0125170330 0.0127948307
110 0.0423283796 0.0125170330
111 0.0116073883 0.0423283796
112 0.0312398849 0.0116073883
113 0.0409862326 0.0312398849
114 -0.0184065674 0.0409862326
115 -0.0001273404 -0.0184065674
116 0.0085618950 -0.0001273404
117 0.0093648143 0.0085618950
118 -0.0418244511 0.0093648143
119 -0.0178845750 -0.0418244511
120 -0.0273381407 -0.0178845750
121 0.0091045739 -0.0273381407
122 -0.0344531142 0.0091045739
123 -0.0337314221 -0.0344531142
124 -0.0201332003 -0.0337314221
125 -0.0042094976 -0.0201332003
126 -0.0042122213 -0.0042094976
127 0.0111223656 -0.0042122213
128 0.0249346931 0.0111223656
129 -0.0217147711 0.0249346931
130 NA -0.0217147711
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0125780798 -0.0126400795
[2,] 0.0082299333 -0.0125780798
[3,] 0.0175137533 0.0082299333
[4,] 0.0116009215 0.0175137533
[5,] 0.0310530210 0.0116009215
[6,] 0.0137058691 0.0310530210
[7,] 0.0030589552 0.0137058691
[8,] 0.0207858155 0.0030589552
[9,] -0.0208192680 0.0207858155
[10,] -0.0150311776 -0.0208192680
[11,] -0.0449945416 -0.0150311776
[12,] -0.0068276592 -0.0449945416
[13,] -0.0163416644 -0.0068276592
[14,] -0.0145190143 -0.0163416644
[15,] 0.0051630660 -0.0145190143
[16,] -0.0125888207 0.0051630660
[17,] -0.0308813214 -0.0125888207
[18,] -0.0322776257 -0.0308813214
[19,] 0.0038884757 -0.0322776257
[20,] -0.0135497984 0.0038884757
[21,] 0.0126725706 -0.0135497984
[22,] 0.0114571913 0.0126725706
[23,] 0.0162566429 0.0114571913
[24,] 0.0263011826 0.0162566429
[25,] 0.0109824197 0.0263011826
[26,] 0.0153316553 0.0109824197
[27,] 0.0075680675 0.0153316553
[28,] -0.0025357623 0.0075680675
[29,] 0.0081183324 -0.0025357623
[30,] 0.0123939120 0.0081183324
[31,] 0.0053642443 0.0123939120
[32,] -0.0085985610 0.0053642443
[33,] -0.0112159390 -0.0085985610
[34,] -0.0163443702 -0.0112159390
[35,] -0.0040995977 -0.0163443702
[36,] -0.0078981690 -0.0040995977
[37,] -0.0111813038 -0.0078981690
[38,] 0.0045588112 -0.0111813038
[39,] 0.0523844833 0.0045588112
[40,] -0.0079682768 0.0523844833
[41,] -0.0072893843 -0.0079682768
[42,] -0.0057155494 -0.0072893843
[43,] -0.0206593051 -0.0057155494
[44,] 0.0141239967 -0.0206593051
[45,] -0.0007463944 0.0141239967
[46,] 0.0065828816 -0.0007463944
[47,] -0.0081136499 0.0065828816
[48,] -0.0051873949 -0.0081136499
[49,] -0.0003121740 -0.0051873949
[50,] 0.0057551488 -0.0003121740
[51,] -0.0038606632 0.0057551488
[52,] -0.0093450779 -0.0038606632
[53,] 0.0020597576 -0.0093450779
[54,] 0.0331267001 0.0020597576
[55,] 0.0523101396 0.0331267001
[56,] 0.0228515594 0.0523101396
[57,] 0.0105236813 0.0228515594
[58,] -0.0067572186 0.0105236813
[59,] -0.0094879583 -0.0067572186
[60,] 0.0063853680 -0.0094879583
[61,] 0.0397540794 0.0063853680
[62,] -0.0033629466 0.0397540794
[63,] 0.0059914296 -0.0033629466
[64,] 0.0192402357 0.0059914296
[65,] -0.0292055660 0.0192402357
[66,] 0.0016743094 -0.0292055660
[67,] -0.0040904833 0.0016743094
[68,] -0.0155655838 -0.0040904833
[69,] -0.0331006160 -0.0155655838
[70,] -0.0074905606 -0.0331006160
[71,] -0.0140482293 -0.0074905606
[72,] 0.0188431610 -0.0140482293
[73,] -0.0194244301 0.0188431610
[74,] 0.0022958988 -0.0194244301
[75,] -0.0012833625 0.0022958988
[76,] 0.0161128010 -0.0012833625
[77,] 0.0099989916 0.0161128010
[78,] -0.0115918121 0.0099989916
[79,] -0.0433949879 -0.0115918121
[80,] 0.0051359046 -0.0433949879
[81,] 0.0117614174 0.0051359046
[82,] 0.0086466333 0.0117614174
[83,] 0.0006724358 0.0086466333
[84,] -0.0040836233 0.0006724358
[85,] 0.0011170200 -0.0040836233
[86,] 0.0178571664 0.0011170200
[87,] -0.0390672721 0.0178571664
[88,] 0.0217699459 -0.0390672721
[89,] -0.0025149551 0.0217699459
[90,] 0.0029178878 -0.0025149551
[91,] -0.0264845075 0.0029178878
[92,] -0.0013463290 -0.0264845075
[93,] 0.0162901710 -0.0013463290
[94,] 0.0103008668 0.0162901710
[95,] -0.0465076873 0.0103008668
[96,] -0.0137645671 -0.0465076873
[97,] 0.0495800289 -0.0137645671
[98,] 0.0104744828 0.0495800289
[99,] -0.0127127600 0.0104744828
[100,] 0.0070135855 -0.0127127600
[101,] -0.0503187711 0.0070135855
[102,] 0.0017185042 -0.0503187711
[103,] -0.0290974722 0.0017185042
[104,] 0.0093492825 -0.0290974722
[105,] 0.0433078713 0.0093492825
[106,] -0.0213323702 0.0433078713
[107,] 0.0356952361 -0.0213323702
[108,] 0.0127948307 0.0356952361
[109,] 0.0125170330 0.0127948307
[110,] 0.0423283796 0.0125170330
[111,] 0.0116073883 0.0423283796
[112,] 0.0312398849 0.0116073883
[113,] 0.0409862326 0.0312398849
[114,] -0.0184065674 0.0409862326
[115,] -0.0001273404 -0.0184065674
[116,] 0.0085618950 -0.0001273404
[117,] 0.0093648143 0.0085618950
[118,] -0.0418244511 0.0093648143
[119,] -0.0178845750 -0.0418244511
[120,] -0.0273381407 -0.0178845750
[121,] 0.0091045739 -0.0273381407
[122,] -0.0344531142 0.0091045739
[123,] -0.0337314221 -0.0344531142
[124,] -0.0201332003 -0.0337314221
[125,] -0.0042094976 -0.0201332003
[126,] -0.0042122213 -0.0042094976
[127,] 0.0111223656 -0.0042122213
[128,] 0.0249346931 0.0111223656
[129,] -0.0217147711 0.0249346931
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0125780798 -0.0126400795
2 0.0082299333 -0.0125780798
3 0.0175137533 0.0082299333
4 0.0116009215 0.0175137533
5 0.0310530210 0.0116009215
6 0.0137058691 0.0310530210
7 0.0030589552 0.0137058691
8 0.0207858155 0.0030589552
9 -0.0208192680 0.0207858155
10 -0.0150311776 -0.0208192680
11 -0.0449945416 -0.0150311776
12 -0.0068276592 -0.0449945416
13 -0.0163416644 -0.0068276592
14 -0.0145190143 -0.0163416644
15 0.0051630660 -0.0145190143
16 -0.0125888207 0.0051630660
17 -0.0308813214 -0.0125888207
18 -0.0322776257 -0.0308813214
19 0.0038884757 -0.0322776257
20 -0.0135497984 0.0038884757
21 0.0126725706 -0.0135497984
22 0.0114571913 0.0126725706
23 0.0162566429 0.0114571913
24 0.0263011826 0.0162566429
25 0.0109824197 0.0263011826
26 0.0153316553 0.0109824197
27 0.0075680675 0.0153316553
28 -0.0025357623 0.0075680675
29 0.0081183324 -0.0025357623
30 0.0123939120 0.0081183324
31 0.0053642443 0.0123939120
32 -0.0085985610 0.0053642443
33 -0.0112159390 -0.0085985610
34 -0.0163443702 -0.0112159390
35 -0.0040995977 -0.0163443702
36 -0.0078981690 -0.0040995977
37 -0.0111813038 -0.0078981690
38 0.0045588112 -0.0111813038
39 0.0523844833 0.0045588112
40 -0.0079682768 0.0523844833
41 -0.0072893843 -0.0079682768
42 -0.0057155494 -0.0072893843
43 -0.0206593051 -0.0057155494
44 0.0141239967 -0.0206593051
45 -0.0007463944 0.0141239967
46 0.0065828816 -0.0007463944
47 -0.0081136499 0.0065828816
48 -0.0051873949 -0.0081136499
49 -0.0003121740 -0.0051873949
50 0.0057551488 -0.0003121740
51 -0.0038606632 0.0057551488
52 -0.0093450779 -0.0038606632
53 0.0020597576 -0.0093450779
54 0.0331267001 0.0020597576
55 0.0523101396 0.0331267001
56 0.0228515594 0.0523101396
57 0.0105236813 0.0228515594
58 -0.0067572186 0.0105236813
59 -0.0094879583 -0.0067572186
60 0.0063853680 -0.0094879583
61 0.0397540794 0.0063853680
62 -0.0033629466 0.0397540794
63 0.0059914296 -0.0033629466
64 0.0192402357 0.0059914296
65 -0.0292055660 0.0192402357
66 0.0016743094 -0.0292055660
67 -0.0040904833 0.0016743094
68 -0.0155655838 -0.0040904833
69 -0.0331006160 -0.0155655838
70 -0.0074905606 -0.0331006160
71 -0.0140482293 -0.0074905606
72 0.0188431610 -0.0140482293
73 -0.0194244301 0.0188431610
74 0.0022958988 -0.0194244301
75 -0.0012833625 0.0022958988
76 0.0161128010 -0.0012833625
77 0.0099989916 0.0161128010
78 -0.0115918121 0.0099989916
79 -0.0433949879 -0.0115918121
80 0.0051359046 -0.0433949879
81 0.0117614174 0.0051359046
82 0.0086466333 0.0117614174
83 0.0006724358 0.0086466333
84 -0.0040836233 0.0006724358
85 0.0011170200 -0.0040836233
86 0.0178571664 0.0011170200
87 -0.0390672721 0.0178571664
88 0.0217699459 -0.0390672721
89 -0.0025149551 0.0217699459
90 0.0029178878 -0.0025149551
91 -0.0264845075 0.0029178878
92 -0.0013463290 -0.0264845075
93 0.0162901710 -0.0013463290
94 0.0103008668 0.0162901710
95 -0.0465076873 0.0103008668
96 -0.0137645671 -0.0465076873
97 0.0495800289 -0.0137645671
98 0.0104744828 0.0495800289
99 -0.0127127600 0.0104744828
100 0.0070135855 -0.0127127600
101 -0.0503187711 0.0070135855
102 0.0017185042 -0.0503187711
103 -0.0290974722 0.0017185042
104 0.0093492825 -0.0290974722
105 0.0433078713 0.0093492825
106 -0.0213323702 0.0433078713
107 0.0356952361 -0.0213323702
108 0.0127948307 0.0356952361
109 0.0125170330 0.0127948307
110 0.0423283796 0.0125170330
111 0.0116073883 0.0423283796
112 0.0312398849 0.0116073883
113 0.0409862326 0.0312398849
114 -0.0184065674 0.0409862326
115 -0.0001273404 -0.0184065674
116 0.0085618950 -0.0001273404
117 0.0093648143 0.0085618950
118 -0.0418244511 0.0093648143
119 -0.0178845750 -0.0418244511
120 -0.0273381407 -0.0178845750
121 0.0091045739 -0.0273381407
122 -0.0344531142 0.0091045739
123 -0.0337314221 -0.0344531142
124 -0.0201332003 -0.0337314221
125 -0.0042094976 -0.0201332003
126 -0.0042122213 -0.0042094976
127 0.0111223656 -0.0042122213
128 0.0249346931 0.0111223656
129 -0.0217147711 0.0249346931
> 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/wessaorg/rcomp/tmp/770f61333541230.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/87s6n1333541230.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/9pzfa1333541230.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/100tl21333541230.ps",horizontal=F,onefile=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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11or761333541230.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12ah3f1333541230.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/137ust1333541231.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14g2fo1333541231.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/wessaorg/rcomp/tmp/152uua1333541231.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/wessaorg/rcomp/tmp/16n1xc1333541231.tab")
+ }
>
> try(system("convert tmp/1gwtr1333541230.ps tmp/1gwtr1333541230.png",intern=TRUE))
character(0)
> try(system("convert tmp/2l0ea1333541230.ps tmp/2l0ea1333541230.png",intern=TRUE))
character(0)
> try(system("convert tmp/32v641333541230.ps tmp/32v641333541230.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hiaw1333541230.ps tmp/4hiaw1333541230.png",intern=TRUE))
character(0)
> try(system("convert tmp/55pqc1333541230.ps tmp/55pqc1333541230.png",intern=TRUE))
character(0)
> try(system("convert tmp/6lm9l1333541230.ps tmp/6lm9l1333541230.png",intern=TRUE))
character(0)
> try(system("convert tmp/770f61333541230.ps tmp/770f61333541230.png",intern=TRUE))
character(0)
> try(system("convert tmp/87s6n1333541230.ps tmp/87s6n1333541230.png",intern=TRUE))
character(0)
> try(system("convert tmp/9pzfa1333541230.ps tmp/9pzfa1333541230.png",intern=TRUE))
character(0)
> try(system("convert tmp/100tl21333541230.ps tmp/100tl21333541230.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.221 0.717 6.954