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12.9 0 0 21 21 149 18 68 96 86
7.4 0 0 23 26 152 7 55 75 62
12.2 0 1 22 22 139 31 39 70 70
12.8 0 0 21 22 148 39 32 88 71
7.4 0 1 21 18 158 46 62 114 108
6.7 0 1 21 23 128 31 33 69 64
12.6 0 1 21 12 224 67 52 176 119
14.8 0 0 21 20 159 35 62 114 97
13.3 0 1 23 22 105 52 77 121 129
11.1 0 1 22 21 159 77 76 110 153
8.2 0 1 25 19 167 37 41 158 78
11.4 0 1 21 22 165 32 48 116 80
6.4 0 1 23 15 159 36 63 181 99
10.6 0 1 22 20 119 38 30 77 68
12.0 0 0 21 19 176 69 78 141 147
6.3 0 0 21 18 54 21 19 35 40
11.3 1 0 25 15 91 26 31 80 57
11.9 0 1 21 20 163 54 66 152 120
9.3 0 0 21 21 124 36 35 97 71
9.6 1 1 20 21 137 42 42 99 84
10.0 0 0 24 15 121 23 45 84 68
6.4 0 1 23 16 153 34 21 68 55
13.8 0 1 21 23 148 112 25 101 137
10.8 0 0 24 21 221 35 44 107 79
13.8 0 1 23 18 188 47 69 88 116
11.7 0 1 21 25 149 47 54 112 101
10.9 0 1 22 9 244 37 74 171 111
16.1 1 1 20 30 148 109 80 137 189
13.4 1 0 18 20 92 24 42 77 66
9.9 0 1 21 23 150 20 61 66 81
11.5 0 0 22 16 153 22 41 93 63
8.3 0 0 22 16 94 23 46 105 69
11.7 0 0 21 19 156 32 39 131 71
6.1 0 1 23 25 146 7 63 89 70
9.0 0 1 21 25 132 30 34 102 64
9.7 0 1 25 18 161 92 51 161 143
10.8 0 1 22 23 105 43 42 120 85
10.3 0 1 22 21 97 55 31 127 86
10.4 0 0 20 10 151 16 39 77 55
12.7 1 1 21 14 131 49 20 108 69
9.3 0 1 21 22 166 71 49 85 120
11.8 0 0 21 26 157 43 53 168 96
5.9 0 1 22 23 111 29 31 48 60
11.4 0 1 21 23 145 56 39 152 95
13.0 0 1 24 24 162 46 54 75 100
10.8 0 1 22 24 163 19 49 107 68
12.3 1 1 22 18 59 23 34 62 57
11.3 0 0 21 23 187 59 46 121 105
11.8 0 1 22 15 109 30 55 124 85
7.9 1 1 19 19 90 61 42 72 103
12.7 0 0 22 16 105 7 50 40 57
12.3 1 1 23 25 83 38 13 58 51
11.6 1 1 20 23 116 32 37 97 69
6.7 1 1 20 17 42 16 25 88 41
10.9 0 1 23 19 148 19 30 126 49
12.1 1 1 20 21 155 22 28 104 50
13.3 0 1 23 18 125 48 45 148 93
10.1 0 1 21 27 116 23 35 146 58
5.7 1 0 22 21 128 26 28 80 54
14.3 0 1 21 13 138 33 41 97 74
8.0 1 0 21 8 49 9 6 25 15
13.3 1 1 19 29 96 24 45 99 69
9.3 0 1 22 28 164 34 73 118 107
12.5 0 0 21 23 162 48 17 58 65
7.6 0 0 21 21 99 18 40 63 58
15.9 0 1 21 19 202 43 64 139 107
9.2 0 0 21 19 186 33 37 50 70
9.1 1 1 21 20 66 28 25 60 53
11.1 0 0 21 18 183 71 65 152 136
13.0 0 1 22 19 214 26 100 142 126
14.5 0 1 22 17 188 67 28 94 95
12.2 1 0 18 19 104 34 35 66 69
12.3 0 0 21 25 177 80 56 127 136
11.4 0 0 23 19 126 29 29 67 58
8.8 1 0 19 22 76 16 43 90 59
14.6 1 1 19 23 99 59 59 75 118
7.3 0 1 23 26 157 58 52 96 110
12.6 0 0 21 14 139 32 50 128 82
13.0 0 0 21 16 162 43 59 146 102
12.6 1 1 21 24 108 38 27 69 65
13.2 0 0 20 20 159 29 61 186 90
9.9 1 0 19 12 74 36 28 81 64
7.7 0 1 21 24 110 32 51 85 83
10.5 1 0 19 22 96 35 35 54 70
13.4 1 0 19 12 116 21 29 46 50
10.9 1 0 19 22 87 29 48 106 77
4.3 1 1 20 20 97 12 25 34 37
10.3 1 0 19 10 127 37 44 60 81
11.8 1 1 19 23 106 37 64 95 101
11.2 1 1 19 17 80 47 32 57 79
11.4 1 0 20 22 74 51 20 62 71
8.6 1 0 19 24 91 32 28 36 60
13.2 1 0 18 18 133 21 34 56 55
12.6 1 1 19 21 74 13 31 54 44
5.6 1 1 21 20 114 14 26 64 40
9.9 1 1 18 20 140 -2 58 76 56
8.8 1 0 18 22 95 20 23 98 43
7.7 1 1 19 19 98 24 21 88 45
9.0 1 0 21 20 121 11 21 35 32
7.3 1 1 20 26 126 23 33 102 56
11.4 1 1 24 23 98 24 16 61 40
13.6 1 1 22 24 95 14 20 80 34
7.9 1 1 21 21 110 52 37 49 89
10.7 1 1 21 21 70 15 35 78 50
10.3 1 0 19 19 102 23 33 90 56
8.3 1 1 19 8 86 19 27 45 46
9.6 1 1 20 17 130 35 41 55 76
14.2 1 1 18 20 96 24 40 96 64
8.5 1 0 19 11 102 39 35 43 74
13.5 1 0 19 8 100 29 28 52 57
4.9 1 0 20 15 94 13 32 60 45
6.4 1 0 21 18 52 8 22 54 30
9.6 1 0 18 18 98 18 44 51 62
11.6 1 0 19 19 118 24 27 51 51
11.1 1 1 19 19 99 19 17 38 36

library(lattice)
library(lmtest)
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
k <- length(x[1,])
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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')
}