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0.36	105.43	2.2	0.769	362	11
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23.72	314.56	20.34	0.857	850	11
5.05	87.46	-6.56	0.583	7	20
1.27	195.26	11.9	0.714	589	12
4.91	212.21	13.26	0.769	589	16
12.79	217.77	14.91	0.538	693	15
0.74	130.73	-3.96	0.538	372	14
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7.16	269.85	14.99	0.615	734	12
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13.08	209.47	14.97	0.846	571	17
6.03	167.70	10.58	0.857	274	16
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8.09	117.22	-6.66	0.571	523	14
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18.88	253.33	11.63	0.769	689	11
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1.18	168.01	-2.53	0.462	591	14
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2.46	163.59	6.74	0.615	667	13
14.42	237.51	16.8	0.692	645	16
13.59	218.64	15.04	0.769	531	9
0.2	202.67	12.02	0.786	656	13
11.14	95.77	2.19	0.615	681	11
6.56	210.17	10.26	0.692	874	12
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12.73	206.74	0.14	0.462	667	17
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1.55	113.85	-1.06	0.786	524	14
7.3	176.89	0.8	0.417	494	13
14.33	263.99	8.56	0.615	882	19
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20.73	289.70	20.44	0.933	863	15
17.84	247.85	10.21	0.615	850	13
8.5	204.71	1.1	0.538	555	16
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11.05	238.13	22.22	0.867	629	12
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6.46	214.23	2.46	0.538	849	15
6.32	189.69	2.74	0.462	696	15
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6.26	148.86	-2.1	0.538	285	14
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5.88	176.61	-10.9	0.333	90	12
11.53	274.79	14.39	0.692	805	14
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2.13	135.65	-10.91	0.25	555	13
18.81	229.36	12.42	0.615	616	13
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1.09	260.72	3.98	0.462	881	13
9.4	265.43	9.96	0.615	709	15
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10.16	132.36	-1.79	0.692	508	13
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8.73	256.97	14.4	0.769	576	18
11.39	181.67	9.84	0.692	644	14
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5.04	219.78	6.71	0.538	712	16
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9.73	217.20	6.52	0.571	695	10
6.55	175.97	-1.54	0.25	510	14
7.59	199.34	8.07	0.538	719	14
9.62	115.28	1.99	0.615	41	16
4.77	128.92	-1.79	0.615	537	16
10.85	185.98	12.7	0.786	663	13
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2.2	83.02	-14.88	0.167	352	15
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20.34	311.91	20.06	0.846	838	12
-0.3	86.84	-15.82	0.167	80	14
11.9	190.38	12.6	0.615	579	12
13.26	190.27	17.5	0.714	579	10
14.91	218.75	-1.32	0.25	686	15
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14.99	270.74	18.8	0.857	726	14
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14.97	203.11	16.87	0.846	560	9
10.58	164.82	3.88	0.615	262	14
6.04	156.13	1.28	0.538	644	10
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4.38	150.20	10.23	0.615	538	13
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2.55	204.97	-8.58	0.083	642	14
1.98	143.22	8.42	0.923	118	15
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2.42	167.71	0.41	0.692	575	13
11.63	251.31	14.98	0.846	679	12
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5.47	109.84	-0.23	0.571	488	13
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6.64	156.61	7.86	0.714	474	14
2.35	140.09	4.3	0.769	415	9
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8.12	276.42	4.67	0.333	684	14
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14.48	283.34	23.36	1	499	13
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18.84	270.99	12.82	0.615	767	15
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15.92	167.69	7.21	0.538	700	11
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0.85	155.24	4.93	0.615	396	17
-19.67	67.11	-19.3	0.083	309	15
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1.37	186.17	7.31	0.615	606	13
-8.64	159.59	-3.57	0.25	509	16
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12.73	159.79	10.01	0.615	495	10
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1.55	197.10	-3.51	0.167	582	16
10.75	275.37	14.59	0.769	753	12
5.46	93.69	-15.66	0.333	526	13
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10.52	194.42	9.99	0.923	478	13
12.55	155.70	3.07	0.714	410	13
1.58	186.49	0.64	0.538	623	17
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7.22	123.34	-7.41	0.25	440	17
8.04	242.36	6.59	0.692	582	13
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1.82	252.35	5.53	0.538	910	15
16.07	204.42	14.74	0.929	659	11
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6.74	161.75	5.27	0.615	659	14
16.8	232.98	10.42	0.615	636	15
15.04	207.30	7.72	0.538	521	16
12.02	187.78	19.97	0.857	645	9
2.19	72.99	4.26	0.692	673	15
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0.14	211.04	6.22	0.538	661	15
4.17	169.67	-5.66	0.25	560	15
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8.56	264.70	9.5	0.692	874	11
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20.44	289.77	15.65	0.857	849	12
10.21	249.34	13.19	0.846	842	14
1.1	210.07	16.83	0.769	548	9
22.22	241.14	21.27	0.846	616	15
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3.19	193.96	-5.68	0.462	634	16
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6.7	238.05	16.06	0.846	577	14
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14.39	269.46	13.72	0.714	796	14
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12.42	229.70	18.96	0.786	608	11
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18.96	201.16	1.94	0.333	603	15
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14.4	232.51	17.1	0.769	566	16
9.84	181.38	6.96	0.417	635	11
3.63	79.77	6.7	0.643	504	7
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4.83	212.04	-4.94	0.167	632	15
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6.52	218.21	15.12	0.692	687	12
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8.07	191.19	-3.74	0.333	712	14
1.99	102.85	-0.52	0.667	33	11
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20.06	302.35	24.51	0.929	827	13
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12.6	182.14	9.38	0.615	571	17
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2.29	93.67	-1.6	0.692	421	14
16.87	197.19	11.22	0.615	549	13
3.88	151.05	7.85	0.846	254	13
1.28	157.62	-6.74	0.167	637	14
4.89	106.16	-0.27	0.615	505	19
10.23	161.53	8.77	0.615	530	14
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0.41	170.14	8.71	0.769	566	14
14.98	243.53	14.75	0.846	668	13
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7.86	154.00	-0.28	0.462	464	14
4.3	138.09	-4.23	0.615	405	15
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4.67	290.40	17.35	0.846	680	11
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23.36	277.99	13.56	0.857	485	10
5.04	102.97	6.33	0.692	568	13
12.82	265.42	17.93	0.857	759	13
7.21	172.40	5.18	0.5	693	16
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4.93	160.76	-5.54	0.417	388	14
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7.31	192.48	-2.62	0.333	598	14
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9.99	194.64	5.88	0.846	466	15
3.07	151.05	-6.04	0.417	400	15
0.64	187.41	-6.59	0.333	616	12
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6.59	238.91	4.07	0.583	573	19
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5.53	251.99	10.01	0.615	903	12
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5.27	161.06	-3.25	0.462	651	16
10.42	227.24	8.43	0.538	628	18
7.72	208.26	6.15	0.615	514	13
19.97	193.73	5.54	0.417	633	12
4.26	58.43	-1.76	0.615	664	11
5.22	221.86	9.79	0.714	856	13
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6.22	212.09	4.52	0.667	654	15
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3.56	90.76	4.64	0.857	501	12
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9.5	257.70	16.98	0.923	865	14
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15.65	274.16	13.81	1	837	13
13.19	257.67	14.32	0.769	831	11
16.83	207.19	13.5	0.615	538	14
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8.11	180.64	12.14	0.692	513	15
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2.81	156.99	6.27	0.615	699	13
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13.38	234.04	20.05	0.846	692	10
1.94	196.76	7.49	0.538	599	15
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5.32	207.36	6.33	0.615	532	13
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8.01	183.71	9.88	0.692	573	13
-4.94	201.56	-3.84	0.333	630	14
6.41	211.02	3.13	0.538	697	13
0	151.10	-6.2	0.417	423	16
15.12	226.46	4.9	0.538	678	17
5.09	164.06	-6.89	0.25	501	15
-3.74	193.76	4.03	0.538	708	9
-0.52	99.43	-1.5	0.538	25	13
-19.16	107.94	-9.54	0.333	528	10
14.2	179.45	9.77	0.571	643	15
-9.36	86.16	-10.78	0.333	500	14

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')
}
}