• QR Code (size=2)
• QR Code (size=3)
• QR Code (size=4)
• QR Code (size=5)
• QR Code (size=6)

-0.24	0.13	0.08
-0.51	0.10	0.01
0.78	0.21	0.08
-0.30	0.16	0.02
0.11	0.12	0.03
1.37	0.17	0.05
-0.07	0.18	0.03
-0.23	0.13	0.01
0.74	0.20	0.05
0.14	0.83	0.04
0.06	0.83	0.02
-0.39	0.80	-0.02
-0.15	0.81	-0.02
0.11	0.86	0.01
-0.23	0.85	-0.01
-0.12	0.86	-0.06
-0.12	0.86	-0.09
-0.35	0.83	-0.05
-0.45	0.88	-0.10
-0.05	0.87	0.12
-0.21	0.91	0.08
0.20	0.92	0.10
0.06	0.93	0.06
-0.23	0.73	-0.01
-0.10	0.67	0.02
0.32	0.67	0.04
0.12	0.70	0.00
-0.30	0.07	0.01
-0.15	0.24	0.08
-0.41	0.23	0.06
-0.23	0.26	0.04
0.21	0.37	0.08
0.40	0.45	0.06
1.86	0.49	0.01
-0.01	0.53	0.01
0.07	0.54	0.06
-0.46	0.51	-0.03
-0.66	0.37	0.03
0.45	0.57	0.00
1.33	0.57	0.07
-0.34	0.55	0.05
-0.17	0.59	-0.03
1.21	0.60	0.02
-0.36	0.67	0.02
-0.14	0.62	0.02
-0.46	0.25	0.18
-0.65	0.18	0.19
0.98	0.21	0.18
0.68	0.25	0.23
-0.47	0.29	0.25
0.55	0.32	0.27
0.79	0.35	0.15
-0.41	0.42	0.09
-0.30	0.43	0.13
1.29	0.72	0.34
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0.22	0.76	0.08
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-0.18	0.82	-0.41
0.08	0.84	-0.37
-0.38	0.85	-0.54
0.00	0.79	-1.02
-0.68	0.51	-0.21
-0.83	0.33	-0.46
4.62	0.34	-0.09
0.09	0.32	-0.02
0.41	0.49	0.05
1.55	0.78	0.09
1.93	0.74	0.08
-0.40	0.75	0.10
0.18	0.51	0.03
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0.20	0.57	0.03
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0.01	0.25	0.03
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0.03	0.30	0.03
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0.20	0.35	0.04
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0.95	0.71	0.13
0.15	0.70	0.15
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0.10	0.41	0.13
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1.51	0.79	0.19
0.62	0.78	0.19
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0.04	0.67	0.18
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0.77	0.71	0.11
0.64	0.69	0.17
-0.11	0.67	0.19
0.08	0.65	0.15
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0.01	0.39	0.10
0.19	0.36	0.11
-0.35	0.73	0.06
-0.38	0.55	0.06
0.13	0.69	0.03
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1.08	0.65	-0.02
0.25	0.40	-0.02
0.31	0.39	0.03
1.19	1.29	0.00
-0.32	2.20	-0.14
1.64	2.23	-0.22
0.76	1.90	0.10
-0.09	0.97	0.15
-0.38	0.27	-0.02
-0.02	0.30	-0.08
0.42	0.37	0.03
-0.33	2.10	-0.21
0.05	0.89	0.12
-0.28	0.85	0.10
-0.16	0.89	0.02
0.11	0.90	0.05
0.16	0.92	0.08
-0.08	0.92	0.06
-0.02	0.91	0.04
-0.25	0.91	0.00
0.57	0.92	0.03
1.58	0.75	-0.14
-0.61	0.71	-0.20
0.42	0.70	-0.30
-0.12	0.65	-0.27
-0.52	0.59	-0.07
-0.50	0.68	-0.08
-0.12	0.64	-0.27
-0.21	0.60	-0.39
-0.67	0.76	-0.35
-0.20	0.82	0.07
-0.75	0.56	0.01
1.41	0.69	-0.12
0.41	0.77	0.15
0.17	0.84	0.17
0.05	0.86	0.14
0.26	0.86	0.14
0.14	0.42	0.02
0.00	0.48	0.11
1.04	0.13	0.05
-0.25	0.20	0.06
0.57	0.30	0.06
0.25	0.34	0.05
-0.17	0.16	0.05
0.21	0.40	0.03
0.30	0.41	0.03
0.21	0.37	0.04
0.03	0.33	0.04
-0.89	0.18	-0.05
-0.74	0.19	-0.19
3.80	0.05	-0.20
0.41	0.16	-0.21
-0.05	0.27	-0.23
0.06	0.16	-0.53
0.72	0.34	0.06
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0.78	0.71	0.29
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0.60	0.74	0.27
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0.31	0.73	0.20
0.02	0.73	0.22
0.15	0.75	0.22
0.15	0.73	0.24
-0.31	0.74	0.19
-0.20	0.49	-6.39
0.17	0.30	-4.03
-0.75	-0.15	-3.17
-0.37	0.81	0.23
0.27	0.91	-4.45
0.03	0.64	0.12
-0.40	0.55	0.09
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0.56	0.63	0.09
0.53	0.64	0.11
0.51	0.65	0.13
0.09	0.63	0.14
0.13	0.73	0.13
-0.22	0.72	0.13
0.53	0.42	0.16
-0.16	0.60	0.19
0.21	0.60	0.19
1.06	0.65	0.23
-0.52	0.49	0.19
0.80	0.51	0.17
1.02	0.51	0.09
0.71	0.44	0.28
0.02	0.51	0.28
0.04	0.90	-0.02
-0.44	0.91	0.02
-0.44	0.82	-1.57
0.26	0.80	-0.07
0.21	0.82	0.12
-0.03	0.86	0.04
0.04	0.86	-0.02
0.60	0.88	0.06
0.66	0.86	0.19
-0.31	0.82	-0.04
-0.70	0.72	-0.04
-0.15	0.54	-0.08
0.20	0.51	-0.02
0.14	0.59	-0.02
0.29	0.61	-0.03
-0.14	0.53	-0.19
-0.13	0.70	-0.28
-0.27	0.60	-0.24
1.36	0.67	0.08
-0.21	0.67	0.08
-0.53	0.64	-0.01
0.15	0.62	0.05
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-0.14	0.69	0.01
0.43	0.64	0.04
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0.62	0.79	0.04
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0.21	0.55	0.05
0.96	0.62	0.06
-0.13	0.65	0.04
0.19	0.64	0.03
0.05	0.75	0.10
-0.55	0.81	0.06
-0.33	0.85	-0.06
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0.63	0.86	0.06
-0.10	0.85	0.02
0.14	0.84	0.00
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0.17	0.78	0.02
0.07	0.38	0.04
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1.14	0.39	0.05
0.25	0.35	0.04
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0.38	0.24	0.05
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0.23	0.66	0.04
0.27	0.80	0.09
0.59	0.85	0.11
-0.31	0.86	0.12
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0.23	0.88	-0.04
0.29	0.90	0.08
-0.31	0.45	0.34
-0.22	0.36	0.34
-0.02	0.40	0.28
0.40	0.42	0.27
-0.11	0.44	0.26
0.19	0.47	0.25
0.98	0.51	0.16
-0.02	0.41	0.22
-0.23	0.39	0.07
0.18	0.77	0.14
-0.07	0.83	0.13
-0.04	0.84	0.13
0.17	0.83	0.15
0.12	0.72	0.05
0.23	0.65	0.15
1.01	0.69	0.17
0.15	0.78	0.14
0.51	-0.71	0.20
0.97	-0.13	0.14
-0.94	-0.26	0.14
6.94	-0.28	0.03
0.48	-0.29	0.22
0.31	-0.32	0.16
0.35	0.00	0.26
4.26	-0.01	0.21
-0.07	0.04	0.27
0.13	0.05	0.23
0.46	0.48	-0.02
-0.60	0.44	-0.18
1.30	0.39	-0.02
-0.41	0.40	-0.03
-0.38	0.50	0.04
0.29	0.53	0.00
0.35	0.34	-0.03
0.01	0.32	-0.03
-0.26	0.27	-0.02
0.46	0.47	0.06
0.25	0.45	0.07
-0.45	0.34	0.06
0.51	0.36	0.06
0.09	0.40	0.06
0.20	0.40	0.05
0.63	0.30	0.07
0.15	0.31	0.09
0.22	0.36	0.09
0.23	0.81	0.03
-0.12	0.80	0.03
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0.28	0.83	-0.07
0.45	0.79	0.07
-0.14	0.78	0.10
0.43	0.77	0.11
0.62	0.78	0.05
-0.17	0.64	0.00
-0.99	0.38	0.46
-0.52	0.30	0.47
0.58	0.28	0.50
0.40	0.27	0.43
-0.07	0.30	0.47
0.02	0.22	0.47
0.00	0.27	0.41
-0.04	0.28	0.18
-0.15	0.28	0.39
-0.65	0.38	0.31
-0.81	0.28	0.24
5.73	0.24	-0.02
-0.62	0.23	0.05
-0.17	0.29	0.07
-0.24	0.25	0.17
4.28	0.21	0.16
-0.57	0.17	0.17
0.01	-0.04	0.08
-0.17	0.43	0.27
-0.61	0.47	0.19
1.53	0.56	0.23
-0.09	0.39	0.11
0.27	0.37	0.03
-0.14	0.42	0.06
0.33	0.53	0.03
0.31	0.58	0.01
0.10	0.69	0.25
0.20	0.78	-0.08
-0.30	0.76	-0.04
-0.16	0.74	0.05
0.09	0.67	0.01
0.28	0.69	0.01
-0.13	0.71	-0.01
0.55	0.76	-0.03
0.07	0.80	-0.01
-0.41	0.81	-0.23
0.64	0.83	0.16
-0.58	0.83	0.17
0.60	0.85	0.06
0.89	0.85	0.11
-0.07	0.83	0.19
1.64	0.91	0.08
0.68	0.90	-0.06
-0.38	0.87	-0.20
-0.72	0.84	-1.97
0.44	0.76	0.19
-0.40	0.77	0.24
0.65	0.85	0.30
0.26	0.83	0.23
-0.02	0.83	0.22
0.15	0.78	0.29
0.70	0.72	0.32
-0.01	0.77	0.29
0.01	0.81	0.29
-0.15	0.68	0.09
-0.36	0.58	0.07
0.59	0.64	0.08
0.49	0.71	0.09
-0.13	0.67	0.09
0.11	0.67	0.09
0.65	0.68	0.08
0.22	0.65	0.08
0.07	0.66	0.08
-0.38	0.86	0.20
-0.42	0.83	0.13
-0.46	0.87	-0.53
-0.07	0.83	-3.07
-0.02	0.77	-3.61
-0.59	0.67	-3.72
-0.29	0.35	-4.72
-0.27	0.38	-5.30
1.12	-0.12	-7.03
0.03	0.66	0.09
-0.55	0.61	0.10
1.21	0.63	0.03
0.29	0.47	0.11
-0.27	0.53	0.06
-0.16	0.45	-0.11
-0.07	0.42	0.05
-0.12	0.45	0.04
-0.10	0.31	0.03
-0.11	0.75	0.13
0.19	0.75	0.15
-0.46	0.79	0.16
1.03	0.79	0.17
0.07	0.79	0.18
-0.22	0.72	0.12
0.62	0.75	0.13
-0.16	0.76	0.14
-0.03	0.70	0.11
-0.34	0.88	-7.30
-0.67	0.82	-4.69
1.33	0.82	-2.25
-0.39	0.88	-3.58
1.18	0.88	-0.26
-0.34	0.92	-1.34
-0.59	0.93	-4.06
-0.11	0.90	-1.09
-0.61	0.91	-0.45
-0.09	0.78	0.10
-0.48	0.82	0.08
0.38	0.84	0.06
-0.01	0.84	0.12
-0.03	0.85	0.16
0.23	0.85	0.11
1.03	0.78	0.13
0.25	0.63	0.03
-0.34	0.64	-0.96
-0.43	0.25	0.01
-0.23	0.20	0.03
-0.17	0.17	0.00
0.47	0.20	0.03
-0.44	0.23	0.03
0.27	0.30	0.02
1.06	0.36	0.03
0.23	0.38	0.03
-0.01	0.44	0.03
-0.48	0.61	0.01
-0.49	0.60	-0.03
0.77	0.70	0.02
0.31	0.72	0.02
0.10	0.76	0.03
-0.08	0.78	0.04
0.32	0.79	0.02
-0.12	0.75	0.01
-0.32	0.76	0.01
99.2	96.7	101.0
99.0	98.1	100.1
100.0	100.0	100.0
111.6	104.9	90.6
122.2	104.9	86.5
117.6	109.5	89.7
121.1	110.8	90.6
136.0	112.3	82.8
154.2	109.3	70.1
153.6	105.3	65.4
158.5	101.7	61.3
140.6	95.4	62.5
136.2	96.4	63.6
168.0	97.6	52.6
154.3	102.4	59.7
149.0	101.6	59.5
165.5	103.8	61.3

par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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')
}
}