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12.9 11 18 4 149 13 12 1 0 21
12.2 19 23 4 139 8 8 1 1 22
12.8 16 22 5 148 14 11 1 0 22
7.4 24 22 4 158 16 13 1 1 18
6.7 15 19 4 128 14 11 1 1 23
12.6 17 25 9 224 13 10 1 1 12
14.8 19 28 8 159 15 7 1 0 20
13.3 19 16 11 105 13 10 1 1 22
11.1 28 28 4 159 20 15 1 1 21
8.2 26 21 4 167 17 12 1 1 19
11.4 15 22 6 165 15 12 1 1 22
6.4 26 24 4 159 16 10 1 1 15
10.6 16 24 8 119 12 10 1 1 20
12 24 26 4 176 17 14 1 0 19
6.3 25 28 4 54 11 6 1 0 18
11.3 22 24 11 91 16 12 0 0 15
11.9 15 20 4 163 16 14 1 1 20
9.3 21 26 4 124 15 11 1 0 21
9.6 22 21 6 137 13 8 0 1 21
10 27 28 6 121 14 12 1 0 15
6.4 26 27 4 153 19 15 1 1 16
13.8 26 23 8 148 16 13 1 1 23
10.8 22 24 5 221 17 11 1 0 21
13.8 21 24 4 188 10 12 1 1 18
11.7 22 22 9 149 15 7 1 1 25
10.9 20 21 4 244 14 11 1 1 9
16.1 21 25 7 148 14 7 0 1 30
13.4 20 20 10 92 16 12 0 0 20
9.9 22 21 4 150 15 12 1 1 23
11.5 21 26 4 153 17 13 1 0 16
8.3 8 23 7 94 14 9 1 0 16
11.7 22 21 12 156 16 11 1 0 19
9 20 27 7 132 15 12 1 1 25
9.7 24 25 5 161 16 15 1 1 18
10.8 17 23 8 105 16 12 1 1 23
10.3 20 25 5 97 10 6 1 1 21
10.4 23 23 4 151 8 5 1 0 10
12.7 20 19 9 131 17 13 0 1 14
9.3 22 22 7 166 14 11 1 1 22
11.8 19 24 4 157 10 6 1 0 26
5.9 15 19 4 111 14 12 1 1 23
11.4 20 21 4 145 12 10 1 1 23
13 22 27 4 162 16 6 1 1 24
10.8 17 25 4 163 16 12 1 1 24
12.3 14 25 7 59 16 11 0 1 18
11.3 24 23 4 187 8 6 1 0 23
11.8 17 17 7 109 16 12 1 1 15
7.9 23 28 4 90 15 12 0 1 19
12.7 25 25 4 105 8 8 1 0 16
12.3 16 20 4 83 13 10 0 1 25
11.6 18 25 4 116 14 11 0 1 23
6.7 20 21 8 42 13 7 0 1 17
10.9 18 24 4 148 16 12 1 1 19
12.1 23 28 4 155 19 13 0 1 21
13.3 24 20 4 125 19 14 1 1 18
10.1 23 19 4 116 14 12 1 1 27
5.7 13 24 7 128 15 6 0 0 21
14.3 20 21 12 138 13 14 1 1 13
8 20 24 4 49 10 10 0 0 8
13.3 19 23 4 96 16 12 0 1 29
9.3 22 18 4 164 15 11 1 1 28
12.5 22 27 5 162 11 10 1 0 23
7.6 15 25 15 99 9 7 1 0 21
15.9 17 20 5 202 16 12 1 1 19
9.2 19 21 10 186 12 7 1 0 19
9.1 20 23 9 66 12 12 0 1 20
11.1 22 27 8 183 14 12 1 0 18
13 21 24 4 214 14 10 1 1 19
14.5 21 27 5 188 13 10 1 1 17
12.2 16 24 4 104 15 12 0 0 19
12.3 20 23 9 177 17 12 1 0 25
11.4 21 24 4 126 14 12 1 0 19
8.8 20 21 10 76 11 8 0 0 22
14.6 23 23 4 99 9 10 0 1 23
12.6 18 27 4 139 7 5 1 0 14
13 16 25 7 162 15 10 1 0 16
12.6 17 19 5 108 12 12 0 1 24
13.2 24 24 4 159 15 11 1 0 20
9.9 13 25 4 74 14 9 0 0 12
7.7 19 23 4 110 16 12 1 1 24
10.5 20 23 4 96 14 11 0 0 22
13.4 22 25 4 116 13 10 0 0 12
10.9 19 26 4 87 16 12 0 0 22
4.3 21 26 6 97 13 10 0 1 20
10.3 15 16 10 127 16 9 0 0 10
11.8 21 23 7 106 16 11 0 1 23
11.2 24 26 4 80 16 12 0 1 17
11.4 22 25 4 74 10 7 0 0 22
8.6 20 23 7 91 12 11 0 0 24
13.2 21 26 4 133 12 12 0 0 18
12.6 19 22 8 74 12 6 0 1 21
5.6 14 20 11 114 12 9 0 1 20
9.9 25 27 6 140 19 15 0 1 20
8.8 11 20 14 95 14 10 0 0 22
7.7 17 22 5 98 13 11 0 1 19
9 22 24 4 121 16 12 0 0 20
7.3 20 21 8 126 15 12 0 1 26
11.4 22 24 9 98 12 12 0 1 23
13.6 15 26 4 95 8 11 0 1 24
7.9 23 24 4 110 10 9 0 1 21
10.7 20 24 5 70 16 11 0 1 21
10.3 22 27 4 102 16 12 0 0 19
8.3 16 25 5 86 10 12 0 1 8
9.6 25 27 4 130 18 14 0 1 17
14.2 18 19 4 96 12 8 0 1 20
8.5 19 22 7 102 16 10 0 0 11
13.5 25 22 10 100 10 9 0 0 8
4.9 21 25 4 94 14 10 0 0 15
6.4 22 23 5 52 12 9 0 0 18
9.6 21 24 4 98 11 10 0 0 18
11.6 22 24 4 118 15 12 0 0 19
11.1 23 23 4 99 7 11 0 1 19
4.35 20 22 6 48 16 9 1 1 23
12.7 6 24 4 50 16 11 1 1 22
18.1 15 19 8 150 16 12 1 1 21
17.85 18 25 5 154 16 12 1 1 25
16.6 24 26 4 109 12 7 0 0 30
12.6 22 18 17 68 15 12 0 1 17
17.1 21 24 4 194 14 12 1 1 27
19.1 23 28 4 158 15 12 1 0 23
16.1 20 23 8 159 16 10 1 1 23
13.35 20 19 4 67 13 15 1 0 18
18.4 18 19 7 147 10 10 1 0 18
14.7 25 27 4 39 17 15 1 1 23
10.6 16 24 4 100 15 10 1 1 19
12.6 20 26 5 111 18 15 1 1 15
16.2 14 21 7 138 16 9 1 1 20
13.6 22 25 4 101 20 15 1 1 16
18.9 26 28 4 131 16 12 0 1 24
14.1 20 19 7 101 17 13 1 1 25
14.5 17 20 11 114 16 12 1 1 25
16.15 22 26 7 165 15 12 1 0 19
14.75 22 27 4 114 13 8 1 1 19
14.8 20 23 4 111 16 9 1 1 16
12.45 17 18 4 75 16 15 1 1 19
12.65 22 23 4 82 16 12 1 1 19
17.35 17 21 4 121 17 12 1 1 23
8.6 22 23 4 32 20 15 1 1 21
18.4 21 22 6 150 14 11 1 0 22
16.1 25 21 8 117 17 12 1 1 19
11.6 11 14 23 71 6 6 0 1 20
17.75 19 24 4 165 16 14 1 1 20
15.25 24 26 8 154 15 12 1 1 3
17.65 17 24 6 126 16 12 1 1 23
16.35 22 22 4 149 16 12 1 0 23
17.65 17 20 7 145 14 11 1 0 20
13.6 26 20 4 120 16 12 1 1 15
14.35 20 18 4 109 16 12 1 0 16
14.75 19 18 4 132 16 12 1 0 7
18.25 21 25 10 172 14 12 1 1 24
9.9 24 28 6 169 14 8 1 0 17
16 21 23 5 114 16 8 1 1 24
18.25 19 20 5 156 16 12 1 1 24
16.85 13 22 4 172 15 12 1 0 19
14.6 24 27 4 68 16 11 0 1 25
13.85 28 24 5 89 16 10 0 1 20
18.95 27 23 5 167 18 11 1 1 28
15.6 22 20 5 113 15 12 1 0 23
14.85 23 22 5 115 16 13 0 0 27
11.75 19 21 4 78 16 12 0 0 18
18.45 18 24 6 118 16 12 0 0 28
15.9 23 26 4 87 17 10 0 1 21
17.1 21 24 4 173 14 10 1 0 19
16.1 22 18 4 2 18 11 1 1 23
19.9 17 17 9 162 9 8 0 0 27
10.95 15 23 18 49 15 12 0 1 22
18.45 21 21 6 122 14 9 0 0 28
15.1 20 21 5 96 15 12 0 1 25
15 26 24 4 100 13 9 0 0 21
11.35 19 22 11 82 16 11 0 0 22
15.95 28 24 4 100 20 15 0 1 28
18.1 21 24 10 115 14 8 0 0 20
14.6 19 24 6 141 12 8 0 1 29
15.4 22 23 8 165 15 11 1 1 25
15.4 21 21 8 165 15 11 1 1 25
17.6 20 24 6 110 15 11 0 1 20
13.35 19 19 8 118 16 13 1 1 20
19.1 11 19 4 158 11 7 1 0 16
15.35 17 23 4 146 16 12 0 1 20
7.6 19 25 9 49 7 8 1 0 20
13.4 20 24 9 90 11 8 0 0 23
13.9 17 21 5 121 9 4 0 0 18
19.1 21 18 4 155 15 11 1 1 25
15.25 21 23 4 104 16 10 0 0 18
12.9 12 20 15 147 14 7 0 1 19
16.1 23 23 10 110 15 12 0 0 25
17.35 22 23 9 108 13 11 0 0 25
13.15 22 23 7 113 13 9 0 0 25
12.15 21 23 9 115 12 10 0 0 24
12.6 20 27 6 61 16 8 0 1 19
10.35 18 19 4 60 14 8 0 1 26
15.4 21 25 7 109 16 11 0 1 10
9.6 24 25 4 68 14 12 0 1 17
18.2 22 21 7 111 15 10 0 0 13
13.6 20 25 4 77 10 10 0 0 17
14.85 17 17 15 73 16 12 0 1 30
14.75 19 22 4 151 14 8 1 0 25
14.1 16 23 9 89 16 11 0 0 4
14.9 19 27 4 78 12 8 0 0 16
16.25 23 27 4 110 16 10 0 0 21
19.25 8 5 28 220 16 14 1 1 23
13.6 22 19 4 65 15 9 0 1 22
13.6 23 24 4 141 14 9 1 0 17
15.65 15 23 4 117 16 10 0 0 20
12.75 17 28 5 122 11 13 1 1 20
14.6 21 25 4 63 15 12 0 0 22
9.85 25 27 4 44 18 13 1 1 16
12.65 18 16 12 52 13 8 0 1 23
19.2 20 25 4 131 7 3 0 0 0
16.6 21 26 6 101 7 8 0 1 18
11.2 21 24 6 42 17 12 0 1 25
15.25 24 23 5 152 18 11 1 1 23
11.9 22 24 4 107 15 9 1 0 12
13.2 22 27 4 77 8 12 0 0 18
16.35 23 25 4 154 13 12 1 0 24
12.4 17 19 10 103 13 12 1 1 11
15.85 15 19 7 96 15 10 0 1 18
18.15 22 24 4 175 18 13 1 1 23
11.15 19 20 7 57 16 9 0 1 24
15.65 18 21 4 112 14 12 0 0 29
17.75 21 28 4 143 15 11 1 0 18
7.65 20 26 12 49 19 14 0 0 15
12.35 19 19 5 110 16 11 1 1 29
15.6 19 23 8 131 12 9 1 1 16
19.3 16 23 6 167 16 12 1 0 19
15.2 18 21 17 56 11 8 0 0 22
17.1 23 26 4 137 16 15 1 0 16
15.6 22 25 5 86 15 12 0 1 23
18.4 23 25 4 121 19 14 1 1 23
19.05 20 24 5 149 15 12 1 0 19
18.55 24 23 5 168 14 9 1 0 4
19.1 25 22 6 140 14 9 1 0 20
13.1 25 27 4 88 17 13 0 1 24
12.85 20 26 4 168 16 13 1 1 20
9.5 23 23 4 94 20 15 1 1 4
4.5 21 22 6 51 16 11 1 1 24
11.85 23 26 8 48 9 7 0 0 22
13.6 23 22 10 145 13 10 1 1 16
11.7 11 17 4 66 15 11 1 1 3
12.4 21 25 5 85 19 14 0 1 15
13.35 27 22 4 109 16 14 1 0 24
11.4 19 28 4 63 17 13 0 0 17
14.9 21 22 4 102 16 12 0 1 20
19.9 16 21 16 162 9 8 0 0 27
11.2 21 24 7 86 11 13 0 1 26
14.6 22 26 4 114 14 9 0 1 23
17.6 16 26 4 164 19 12 1 0 17
14.05 18 24 14 119 13 13 1 1 20
16.1 23 27 5 126 14 11 1 0 22
13.35 24 22 5 132 15 11 1 1 19
11.85 20 23 5 142 15 13 1 1 24
11.95 20 22 5 83 14 12 1 0 19
14.75 18 23 7 94 16 12 0 1 23
15.15 4 15 19 81 17 10 0 0 15
13.2 14 20 16 166 12 9 1 1 27
16.85 22 22 4 110 15 10 0 0 26
7.85 17 25 4 64 17 13 0 1 22
7.7 23 27 7 93 15 13 1 0 22
12.6 20 24 9 104 10 9 0 0 18
7.85 18 21 5 105 16 11 0 1 15
10.95 19 17 14 49 15 12 0 1 22
12.35 20 26 4 88 11 8 0 0 27
9.95 15 20 16 95 16 12 0 1 10
14.9 24 22 10 102 16 12 0 1 20
16.65 21 24 5 99 16 12 0 0 17
13.4 19 23 6 63 14 9 0 1 23
13.95 19 22 4 76 14 12 0 0 19
15.7 27 28 4 109 16 12 0 0 13
16.85 23 21 4 117 16 11 0 1 27
10.95 23 24 5 57 18 12 0 1 23
15.35 20 28 4 120 14 6 0 0 16
12.2 17 25 4 73 20 7 0 1 25
15.1 21 24 5 91 15 10 0 0 2
17.75 23 24 4 108 16 12 0 0 26
15.2 22 21 4 105 16 10 0 1 20
14.6 16 20 5 117 16 12 1 0 23
16.65 20 26 8 119 12 9 0 0 22
8.1 16 16 15 31 8 3 0 1 24

library(lattice)
library(lmtest)
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'
}
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,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$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}