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1 0.5 0.67 0.67 0 0.5 2011 1 0 149
0.89 0.5 0.83 0.33 0.5 1 2011 1 1 139
0.89 0.4 1 0.67 0 1 2011 1 0 148
0.89 0.5 0.83 0 0 0 2011 1 1 158
0.89 0.7 0.67 0 1 1 2011 1 1 128
0.78 0.3 0 0 0.5 0.5 2011 1 1 224
0.89 0.4 0.83 0.67 0.5 0 2011 1 0 159
1 0.4 0.5 0.67 1 1 2011 1 1 105
0.89 0.7 0.83 0 0.5 0 2011 1 1 159
0.78 0.6 0.33 0.67 0.5 0.5 2011 1 1 167
1 0.6 0.5 1 0 0.5 2011 1 1 165
0.78 0.2 0.67 0 0.5 0.5 2011 1 1 159
0.89 0.4 1 0 0.5 0.5 2011 1 1 119
0.89 0.4 0.5 0.67 0 1 2011 1 0 176
0.89 0.5 0.67 0.33 0 0 2011 1 0 54
0.89 0.3 0.17 0.67 0 0.5 2011 0 0 91
0.89 0.4 0.83 0.33 0.5 0.5 2011 1 1 163
0.67 0.7 0.67 0.33 0.5 1 2011 1 0 124
1 0.5 0.67 0.33 0 1 2011 0 1 137
0.78 0.2 0.67 0 0 1 2011 1 0 121
0.78 0.3 0.5 0.67 0 0.5 2011 1 1 153
0.89 0.6 1 0.33 0 1 2011 1 1 148
0.78 0.6 0.83 0.33 0 1 2011 1 0 221
0.89 0.2 0.83 0.33 0 1 2011 1 1 188
0.89 0.7 1 0.67 1 0 2011 1 1 149
0.33 0.2 0.67 0 0 0 2011 1 1 244
1 1 1 0.33 1 1 2011 0 1 148
0.89 0.4 0.83 0.67 0 0.5 2011 0 0 92
0.89 0.4 1 1 0 1 2011 1 1 150
0.67 0.2 0.83 0.67 0 0.5 2011 1 0 153
0.56 0.4 0.67 0.33 0 1 2011 1 0 94
0.89 0.4 0.67 0 0.5 1 2011 1 0 156
0.89 0.7 1 0.67 0.5 0.5 2011 1 1 132
1 0.2 0.67 0.67 0 0.5 2011 1 1 161
0.78 0.6 1 1 0 0.5 2011 1 1 105
0.78 0.3 1 1 0.5 0.5 2011 1 1 97
0.33 0.3 0.5 0.33 0 0 2011 1 0 151
0.78 0.2 0.67 0 0.5 0 2011 0 1 131
0.89 0.5 0.83 0.67 0.5 0.5 2011 1 1 166
0.89 0.7 1 0.67 0.5 1 2011 1 0 157
0.78 0.6 1 0.67 0.5 0.5 2011 1 1 111
0.89 0.4 1 0.67 0.5 1 2011 1 1 145
0.89 0.6 1 0.33 0.5 1 2011 1 1 162
1 0.4 1 1 0 1 2011 1 1 163
0.67 0.3 0.83 0.67 0 1 2011 0 1 59
1 0.5 0.83 0.67 0.5 0.5 2011 1 0 187
0.89 0.2 0.5 0 0 1 2011 1 1 109
0.89 0.3 0.83 0 0.5 1 2011 0 1 90
0.89 0.5 0.17 0 0 1 2011 1 0 105
0.78 0.7 0.83 1 0.5 1 2011 0 1 83
0.89 0.4 1 0.67 1 0.5 2011 0 1 116
0.78 0.3 1 0 0 0.5 2011 0 1 42
0.78 0.2 0.67 0.67 1 1 2011 1 1 148
1 0.5 1 0 0 0.5 2011 0 1 155
0.78 0.4 1 0 0.5 0 2011 1 1 125
1 0.6 1 0.67 1 1 2011 1 1 116
0.78 0.4 0.83 1 0 1 2011 0 0 128
0.67 0.4 0.33 0 0 0.5 2011 1 1 138
0.33 0.2 0.33 0.33 0 0 2011 0 0 49
1 0.9 1 0.67 0.5 1 2011 0 1 96
1 0.8 1 0.67 1 0.5 2011 1 1 164
0.78 0.8 0.83 0 0.5 1 2011 1 0 162
0.67 0.3 1 1 0.5 1 2011 1 0 99
1 0.2 0.83 0.67 0 0.5 2011 1 1 202
0.89 0.4 0.67 0 0.5 1 2011 1 0 186
0.89 0.2 0.83 1 0 1 2011 0 1 66
0.78 0.2 0.67 0.67 0.5 1 2011 1 0 183
1 0.1 0.83 0.67 0 1 2011 1 1 214
0.56 0.4 0.67 1 0.5 0 2011 1 1 188
0.67 0.5 1 0 0.5 0.5 2011 0 0 104
0.89 0.8 0.83 0.33 0.5 1 2011 1 0 177
0.89 0.4 0.67 0.67 0 0.5 2011 1 0 126
0.89 0.6 0.83 0.33 0.5 0.5 2011 0 0 76
0.89 0.5 0.83 0.67 0.5 1 2011 0 1 99
0.78 0.3 0.67 0 0 0 2011 1 0 139
1 0.4 0.33 0 0.5 0 2011 1 0 162
1 0.6 0.83 0.67 0.5 0.5 2011 0 1 108
0.89 0.4 1 0.33 0 0.5 2011 1 0 159
0.44 0.3 0.83 0 0 0 2011 0 0 74
0.78 0.8 0.83 0 1 1 2011 1 1 110
0.89 0.6 0.5 0.33 1 1 2011 0 0 96
0.67 0.3 0.5 0 0 0 2011 0 0 116
0.78 0.5 0.83 0.67 0.5 1 2011 0 0 87
0.78 0.4 1 0.33 0 1 2011 0 1 97
0.33 0.3 0.33 0.67 0 0 2011 0 0 127
0.89 0.7 1 0.33 0 0.5 2011 0 1 106
0.89 0.2 0.67 0.33 0.5 0.5 2011 0 1 80
0.89 0.4 0.83 1 0 1 2011 0 0 74
0.89 0.6 1 0.67 0.5 0.5 2011 0 0 91
0.56 0.6 0.83 0 0 1 2011 0 0 133
0.67 0.6 0.83 0.67 0.5 0.5 2011 0 1 74
0.67 0.4 1 0.33 0.5 1 2011 0 1 114
0.78 0.6 0.83 0 0 1 2011 0 1 140
0.78 0.5 1 0.33 0.5 1 2011 0 0 95
0.78 0.5 0.83 0 0 1 2011 0 1 98
0.89 0.6 0.67 0 0 1 2011 0 0 121
1 0.8 0.83 0.33 0.5 1 2011 0 1 126
0.89 0.5 0.83 0.67 1 0.5 2011 0 1 98
0.89 0.6 0.83 0.67 0.5 1 2011 0 1 95
0.78 0.4 0.83 0.67 0.5 1 2011 0 1 110
1 0.3 0.67 0.67 0.5 1 2011 0 1 70
0.78 0.3 0.83 1 0 0.5 2011 0 0 102
0.67 0.2 0 0 0 0 2011 0 1 86
0.78 0.4 0.83 0 0 0.5 2011 0 1 130
0.89 0.5 1 0 0 0.5 2011 0 1 96
0.67 0.3 0.17 0 0.5 0 2011 0 0 102
0.22 0.4 0.17 0 0.5 0 2011 0 0 100
0.44 0.5 0.5 1 0 0 2011 0 0 94
0.89 0.3 0.5 0.67 0 1 2011 0 0 52
0.67 0.5 1 0 0 0.5 2011 0 0 98
0.89 0.4 0.67 0.67 0 0.5 2011 0 0 118
0.67 0.4 0.83 0.67 0 1 2011 0 1 99
0.78 0.6 1 0 1 1 2012 1 1 48
0.78 0.3 1 0.67 1 1 2012 1 1 50
0.78 0.4 1 0.33 1 0.5 2012 1 1 150
1 0.3 1 1 1 1 2012 1 1 154
0.78 1 1 1 1 1 2012 0 0 109
0.67 0.4 1 0 0 0.5 2012 0 1 68
0.89 0.8 0.83 1 0.5 1 2012 1 1 194
0.89 0.3 1 0.67 1 1 2012 1 0 158
1 0.5 0.83 0.67 0 1 2012 1 1 159
0.78 0.4 1 0 0 0.5 2012 1 0 67
0.67 0.3 0.83 0.67 0 1 2012 1 0 147
0.89 0.5 0.83 1 0 1 2012 1 1 39
0.67 0.3 1 0.67 0 1 2012 1 1 100
0.67 0.3 0.67 0 0 1 2012 1 1 111
1 0.4 0.83 0 0 1 2012 1 1 138
0.67 0.3 1 0 0 0.5 2012 1 1 101
1 0.6 1 0.33 0.5 0.5 2012 0 1 131
0.89 0.6 0.83 0.67 1 1 2012 1 1 101
0.89 0.4 1 1 1 1 2012 1 1 114
1 0.4 1 0 0 0 2012 1 0 165
0.67 0.4 1 0.67 0 0.5 2012 1 1 114
0.44 0.3 0.67 0.67 0.5 1 2012 1 1 111
0.89 0.2 1 0.33 1 0 2012 1 1 75
0.56 0.5 0.83 0.67 0 1 2012 1 1 82
0.78 0.4 1 0.67 1 1 2012 1 1 121
1 0.4 1 0.67 0 0 2012 1 1 32
1 0.4 0.83 0.67 0 1 2012 1 0 150
0.89 0.3 0.67 0.67 0.5 0.5 2012 1 1 117
0.67 0.4 0.83 0.67 1 0.5 2012 0 1 71
0.89 0.2 1 0.33 0.5 1 2012 1 1 165
0.33 0 0 0 0 0 2012 1 1 154
0.89 0.4 1 0.67 0.5 1 2012 1 1 126
0.78 0.6 1 0 1 1 2012 1 0 149
1 0.4 0.67 0.67 0 0.5 2012 1 0 145
0.44 0.4 1 0 0 0.5 2012 1 1 120
0.67 0.4 0.83 0 0.5 0 2012 1 0 109
0.33 0.2 0.17 0 0.5 0 2012 1 0 132
0.89 0.4 0.83 1 1 1 2012 1 1 172
0.89 0.3 0.83 0 0 0.5 2012 1 0 169
1 0.6 0.83 0.67 1 0 2012 1 1 114
0.89 0.6 0.83 1 0 1 2012 1 1 156
0.89 0.4 0.83 0 0 1 2012 1 0 172
1 0.5 1 0.67 1 0.5 2012 0 1 68
0.89 0.4 0.83 0 0.5 1 2012 0 1 89
1 0.6 1 1 1 1 2012 1 1 167
0.78 0.6 0.83 0.67 0.5 1 2012 1 0 113
0.78 0.9 1 0.67 0.5 1 2012 0 0 115
0.67 0.4 0.83 0.67 0.5 0 2012 0 0 78
0.89 0.8 1 1 0.5 1 2012 0 0 118
0.67 0.5 0.83 1 0 1 2012 0 1 87
0.78 0.4 0.83 1 0 0 2012 1 0 173
0.89 0.4 1 0.67 1 0.5 2012 1 1 2
0.89 0.7 1 1 1 0.5 2012 0 0 162
0.78 0.4 1 0.33 1 1 2012 0 1 49
1 0.8 1 0.67 0.5 1 2012 0 0 122
1 0.4 1 1 1 0.5 2012 0 1 96
1 0.3 1 0.67 0 0.5 2012 0 0 100
0.67 0.5 1 0.67 0.5 1 2012 0 0 82
0.89 0.8 1 0.67 1 1 2012 0 1 100
1 0.4 0.83 0.33 0 0.5 2012 0 0 115
1 1 1 1 0.5 0 2012 0 1 141
0.89 0.5 1 0.67 1 1 2012 1 1 165
0.89 0.5 1 0.67 1 1 2012 1 1 165
0.89 0.3 1 0.33 0 1 2012 0 1 110
0.89 0.3 0.83 0.33 0.5 1 2012 1 1 118
0.89 0.3 0.5 0 0 1 2012 1 0 158
1 0.4 0.67 0.33 0.5 0.5 2012 0 1 146
0.67 0.5 1 0.33 0 1 2012 1 0 49
1 0.5 0.67 0.67 0.5 1 2012 0 0 90
0.89 0.4 1 0 0 0 2012 0 0 121
0.89 0.7 1 1 0.5 0 2012 1 1 155
0.89 0.5 0.5 0.33 0 0.5 2012 0 0 104
0.89 0.4 0.67 0.33 1 0 2012 0 1 147
1 0.7 0.67 1 0 1 2012 0 0 110
1 0.7 0.67 1 0 1 2012 0 0 108
1 0.7 0.67 1 0 1 2012 0 0 113
0.89 0.7 0.67 1 0 1 2012 0 0 115
0.89 0.7 0.67 0 0 0 2012 0 1 61
0.89 0.7 1 0.67 0.5 1 2012 0 1 60
0.33 0.1 0.67 0.33 0.5 0 2012 0 1 109
0.67 0.2 0.67 0.67 0.5 1 2012 0 1 68
0.56 0.3 0.33 0.33 0 1 2012 0 0 111
0.44 0.6 0.83 0.33 0 0.5 2012 0 0 77
1 0.8 1 1 1 1 2012 0 1 73
0.89 0.8 1 0.33 0.5 0.5 2012 1 0 151
0.33 0 0.17 0 0 0 2012 0 0 89
0.67 0.3 0.67 0.33 0 1 2012 0 0 78
0.67 0.6 0.83 0.33 0.5 1 2012 0 0 110
1 0.5 0.83 0.67 0 1 2012 1 1 220
0.78 0.7 1 0.33 0 0.5 2012 0 1 65
0.67 0.3 0.83 0 0.5 1 2012 1 0 141
1 0.3 1 0.67 0 0 2012 0 0 117
0.78 0.4 1 0.67 0 0.5 2012 1 1 122
0.89 0.4 0.83 1 0 1 2012 0 0 63
0.89 0.1 0.83 0 0 1 2012 1 1 44
0.89 0.5 1 0.67 0 1 2012 0 1 52
0 0 0 0 0 0 2012 0 0 131
0.67 0.4 1 0.33 0.5 0 2012 0 1 101
1 0.6 0.83 0.67 1 0.5 2012 0 1 42
1 0.4 1 0.33 0.5 1 2012 1 1 152
0.67 0.1 0.33 0 0.5 1 2012 1 0 107
0.89 0.3 0.83 0 0 1 2012 0 0 77
0.89 0.7 0.83 0.67 0 1 2012 1 0 154
0.56 0.3 0.17 0 0 1 2012 1 1 103
0.67 0.5 0.83 0.33 0.5 0 2012 0 1 96
1 0.3 0.83 0.67 1 1 2012 1 1 175
1 0.6 0.67 0.67 0.5 1 2012 0 1 57
1 0.9 1 1 0 1 2012 0 0 112
0.67 0.4 0.83 0 0.5 1 2012 1 0 143
0.44 0.3 1 0 0.5 0.5 2012 0 0 49
0.89 0.9 1 0.67 1 1 2012 1 1 110
0.44 0.5 1 0 0.5 0 2012 1 1 131
0.56 0.3 1 1 0.5 0.5 2012 1 0 167
0.89 0.6 0.83 0.67 0 0.5 2012 0 0 56
0.67 0.2 1 0.33 0 0.5 2012 1 0 137
0.89 0.4 0.83 1 0.5 1 2012 0 1 86
1 0.5 0.83 0.67 0.5 0.5 2012 1 1 121
0.78 0.4 0.83 0.67 0 0.5 2012 1 0 149
0.44 0 0 0 0 0 2012 1 0 168
0.89 0.2 1 0.33 0.5 1 2012 1 0 140
0.89 0.5 1 0.67 0.5 1 2012 0 1 88
0.89 0.3 1 0.67 0 0.5 2012 1 1 168
0.44 0 0 0 0 0 2012 1 1 94
1 0.5 0.83 1 0 1 2012 1 1 51
0.89 0.6 0.83 0.33 0 1 2012 0 0 48
0.67 0.3 0.83 0 0.5 0.5 2012 1 1 145
0.33 0 0 0 0 0 2012 1 1 66
0.78 0.3 0.67 0 0.5 0 2012 0 1 85
0.89 0.5 1 0.67 0.5 1 2012 1 0 109
0.78 0.4 0.67 0 0 1 2012 0 0 63
0.78 0.5 0.83 0.67 0 0.5 2012 0 1 102
0.89 0.7 1 1 1 0.5 2012 0 0 162
0.78 0.8 1 0.67 0.5 1 2012 0 1 86
0.78 0.6 1 0.33 0.5 1 2012 0 1 114
0.67 0.4 0.83 0.33 0 0.5 2012 1 0 164
0.89 0.5 0.83 0.33 0.5 0 2012 1 1 119
0.89 0.5 1 0 0.5 1 2012 1 0 126
0.78 0.3 1 0.33 0 1 2012 1 1 132
1 0.6 1 0 0.5 1 2012 1 1 142
1 0.3 0.67 0.67 0 0.5 2012 1 0 83
0.78 0.6 0.83 1 0.5 0.5 2012 0 1 94
0.78 0.3 0.33 0.33 0 1 2012 0 0 81
0.89 0.7 1 0.67 1 1 2012 1 1 166
0.89 0.7 1 1 0 1 2012 0 0 110
0.67 0.6 0.67 1 0.5 1 2012 0 1 64
1 0.5 1 0.33 0.5 0 2012 1 0 93
0.67 0.5 0.83 0.33 0 0.5 2012 0 0 104
0.56 0.4 0.67 0 0 1 2012 0 1 105
0.78 0.4 1 0.33 1 1 2012 0 1 49
1 0.7 1 1 0 1 2012 0 0 88
0.67 0.2 0.17 0 0.5 0 2012 0 1 95
0.78 0.5 0.83 0.67 0 0.5 2012 0 1 102
0.56 0.4 0.83 0.67 0.5 0 2012 0 0 99
1 0.2 1 0.67 1 1 2012 0 1 63
0.89 0.5 0.67 0.67 0 0 2012 0 0 76
0.44 0.4 0.5 0 0 1 2012 0 0 109
1 0.7 0.67 1 1 1 2012 0 1 117
0.89 0.6 0.83 0.67 1 0 2012 0 1 57
0.78 0.4 0.83 0 0 0 2012 0 0 120
0.89 0.5 1 0.67 1 1 2012 0 1 73
0.11 0 0.17 0 0 0 2012 0 0 91
0.89 0.7 1 0.67 0.5 1 2012 0 0 108
0.89 0.4 0.67 0.67 0 1 2012 0 1 105
1 0.5 0.67 1 0 1 2012 1 0 117
0.89 0.6 0.83 0.67 0 0.5 2012 0 0 119
1 0.8 0.5 0.67 0.5 0.5 2012 0 1 31

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
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)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$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.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')