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14 41 38 13 12 14 53
18 39 32 16 11 18 83
11 30 35 19 15 11 66
12 31 33 15 6 12 67
16 34 37 14 13 16 76
18 35 29 13 10 18 78
14 39 31 19 12 14 53
14 34 36 15 14 14 80
15 36 35 14 12 15 74
15 37 38 15 9 15 76
17 38 31 16 10 17 79
19 36 34 16 12 19 54
10 38 35 16 12 10 67
16 39 38 16 11 16 54
18 33 37 17 15 18 87
14 32 33 15 12 14 58
14 36 32 15 10 14 75
17 38 38 20 12 17 88
14 39 38 18 11 14 64
16 32 32 16 12 16 57
18 32 33 16 11 18 66
11 31 31 16 12 11 68
14 39 38 19 13 14 54
12 37 39 16 11 12 56
17 39 32 17 12 17 86
9 41 32 17 13 9 80
16 36 35 16 10 16 76
14 33 37 15 14 14 69
15 33 33 16 12 15 78
11 34 33 14 10 11 67
16 31 31 15 12 16 80
13 27 32 12 8 13 54
17 37 31 14 10 17 71
15 34 37 16 12 15 84
14 34 30 14 12 14 74
16 32 33 10 7 16 71
9 29 31 10 9 9 63
15 36 33 14 12 15 71
17 29 31 16 10 17 76
13 35 33 16 10 13 69
15 37 32 16 10 15 74
16 34 33 14 12 16 75
16 38 32 20 15 16 54
12 35 33 14 10 12 52
15 38 28 14 10 15 69
11 37 35 11 12 11 68
15 38 39 14 13 15 65
15 33 34 15 11 15 75
17 36 38 16 11 17 74
13 38 32 14 12 13 75
16 32 38 16 14 16 72
14 32 30 14 10 14 67
11 32 33 12 12 11 63
12 34 38 16 13 12 62
12 32 32 9 5 12 63
15 37 35 14 6 15 76
16 39 34 16 12 16 74
15 29 34 16 12 15 67
12 37 36 15 11 12 73
12 35 34 16 10 12 70
8 30 28 12 7 8 53
13 38 34 16 12 13 77
11 34 35 16 14 11 80
14 31 35 14 11 14 52
15 34 31 16 12 15 54
10 35 37 17 13 10 80
11 36 35 18 14 11 66
12 30 27 18 11 12 73
15 39 40 12 12 15 63
15 35 37 16 12 15 69
14 38 36 10 8 14 67
16 31 38 14 11 16 54
15 34 39 18 14 15 81
15 38 41 18 14 15 69
13 34 27 16 12 13 84
12 39 30 17 9 12 80
17 37 37 16 13 17 70
13 34 31 16 11 13 69
15 28 31 13 12 15 77
13 37 27 16 12 13 54
15 33 36 16 12 15 79
15 35 37 16 12 15 71
16 37 33 15 12 16 73
15 32 34 15 11 15 72
14 33 31 16 10 14 77
15 38 39 14 9 15 75
14 33 34 16 12 14 69
13 29 32 16 12 13 54
7 33 33 15 12 7 70
17 31 36 12 9 17 73
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14 32 28 15 12 14 82
13 29 30 13 12 13 80
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12 37 35 16 11 12 73
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11 32 28 15 11 11 66
15 33 39 16 11 15 79
9 40 32 13 12 9 68
16 38 35 17 12 16 76
15 41 39 16 12 15 71
10 36 35 16 11 10 54
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11 31 33 16 14 11 74
13 32 41 17 11 13 88
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14 33 35 13 11 14 69
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12 33 37 13 11 12 54
14 31 27 16 13 14 76
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15 37 38 16 14 15 76
15 36 31 15 13 15 73
13 31 33 16 15 13 79
17 39 32 15 10 17 90
17 44 39 17 11 17 74
19 33 36 15 9 19 81
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13 32 33 16 10 13 71
9 28 32 10 11 9 66
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15 27 30 12 11 15 65
15 37 38 14 12 15 74
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15 35 34 12 8 15 64
13 31 35 11 9 13 69
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16 30 37 15 9 16 84
14 30 35 17 15 14 86
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16 40 31 10 8 16 89
16 32 27 18 13 16 76
11 36 36 13 12 11 60
12 32 31 16 12 12 75
9 35 32 13 9 9 73
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16 34 38 16 9 16 71
12 35 39 16 6 12 72
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13 33 31 10 8 13 78
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19 33 36 15 9 19 81
13 34 32 16 11 13 84
12 32 38 12 8 12 84
13 34 36 13 8 13 69
10 27 26 13 10 10 66
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16 38 33 17 14 16 82
10 34 39 15 10 10 72
11 24 30 10 8 11 54
14 30 33 14 11 14 78
12 26 25 11 12 12 74
9 34 38 13 12 9 82
9 27 37 16 12 9 73
11 37 31 12 5 11 55
16 36 37 16 12 16 72
9 41 35 12 10 9 78
13 29 25 9 7 13 59
16 36 28 12 12 16 72
13 32 35 15 11 13 78
9 37 33 12 8 9 68
12 30 30 12 9 12 69
16 31 31 14 10 16 67
11 38 37 12 9 11 74
14 36 36 16 12 14 54
13 35 30 11 6 13 67
15 31 36 19 15 15 70
14 38 32 15 12 14 80
16 22 28 8 12 16 89
13 32 36 16 12 13 76
14 36 34 17 11 14 74
15 39 31 12 7 15 87
13 28 28 11 7 13 54
11 32 36 11 5 11 61
11 32 36 14 12 11 38
14 38 40 16 12 14 75
15 32 33 12 3 15 69
11 35 37 16 11 11 62
15 32 32 13 10 15 72
12 37 38 15 12 12 70
14 34 31 16 9 14 79
14 33 37 16 12 14 87
8 33 33 14 9 8 62
13 26 32 16 12 13 77
9 30 30 16 12 9 69
15 24 30 14 10 15 69
17 34 31 11 9 17 75
13 34 32 12 12 13 54
15 33 34 15 8 15 72
15 34 36 15 11 15 74
14 35 37 16 11 14 85
16 35 36 16 12 16 52
13 36 33 11 10 13 70
16 34 33 15 10 16 84
9 34 33 12 12 9 64
16 41 44 12 12 16 84
11 32 39 15 11 11 87
10 30 32 15 8 10 79
11 35 35 16 12 11 67
15 28 25 14 10 15 65
17 33 35 17 11 17 85
14 39 34 14 10 14 83
8 36 35 13 8 8 61
15 36 39 15 12 15 82
11 35 33 13 12 11 76
16 38 36 14 10 16 58
10 33 32 15 12 10 72
15 31 32 12 9 15 72
9 34 36 13 9 9 38
16 32 36 8 6 16 78
19 31 32 14 10 19 54
12 33 34 14 9 12 63
8 34 33 11 9 8 66
11 34 35 12 9 11 70
14 34 30 13 6 14 71
9 33 38 10 10 9 67
15 32 34 16 6 15 58
13 41 33 18 14 13 72
16 34 32 13 10 16 72
11 36 31 11 10 11 70
12 37 30 4 6 12 76
13 36 27 13 12 13 50
10 29 31 16 12 10 72
11 37 30 10 7 11 72
12 27 32 12 8 12 88
8 35 35 12 11 8 53
12 28 28 10 3 12 58
12 35 33 13 6 12 66
15 37 31 15 10 15 82
11 29 35 12 8 11 69
13 32 35 14 9 13 68
14 36 32 10 9 14 44
10 19 21 12 8 10 56
12 21 20 12 9 12 53
15 31 34 11 7 15 70
13 33 32 10 7 13 78
13 36 34 12 6 13 71
13 33 32 16 9 13 72
12 37 33 12 10 12 68
12 34 33 14 11 12 67
9 35 37 16 12 9 75
9 31 32 14 8 9 62
15 37 34 13 11 15 67
10 35 30 4 3 10 83
14 27 30 15 11 14 64
15 34 38 11 12 15 68
7 40 36 11 7 7 62
14 29 32 14 9 14 72
 
 
 
 
 
 

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')
}