| Finaal model - vertraagde loonkostindex | *The author of this computation has been verified* | R Software Module: /rwasp_multipleregression.wasp (opens new window with default values) | Title produced by software: Multiple Regression | Date of computation: Wed, 26 Jan 2011 14:15:00 +0000 | | Cite this page as follows: | Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq.htm/, Retrieved Wed, 26 Jan 2011 15:17:00 +0100 | | BibTeX entries for LaTeX users: | @Manual{KEY,
author = {{YOUR NAME}},
publisher = {Office for Research Development and Education},
title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq.htm/},
year = {2011},
}
@Manual{R,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Development Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2011},
note = {{ISBN} 3-900051-07-0},
url = {http://www.R-project.org},
}
| | Original text written by user: | | | IsPrivate? | No (this computation is public) | | User-defined keywords: | | | Dataseries X: | » Textbox « » Textfile « » CSV « | 90,09 85,61 87,703 81,71
100,639 85,52 90,09 87,703
83,042 86,51 100,639 90,09
89,956 86,66 83,042 100,639
89,561 87,27 89,956 83,042
105,38 87,62 89,561 89,956
86,554 88,17 105,38 89,561
93,131 87,99 86,554 105,38
92,812 88,83 93,131 86,554
102,195 88,75 92,812 93,131
88,925 88,81 102,195 92,812
94,184 89,43 88,925 102,195
94,196 89,5 94,184 88,925
108,932 89,34 94,196 94,184
91,134 89,75 108,932 94,196
97,149 90,26 91,134 108,932
96,415 90,32 97,149 91,134
112,432 90,76 96,415 97,149
92,47 91,53 112,432 96,415
98,61410515 92,35 92,47 112,432
97,80117197 93,04 98,61410515 92,47
111,8560178 93,35 97,80117197 98,61410515
95,63981455 93,54 111,8560178 97,80117197
104,1120262 95,07 95,63981455 111,8560178
104,0148224 95,39 104,1120262 95,63981455
118,1743476 95,43 104,0148224 104,1120262
102,033431 96,09 118,1743476 104,0148224
109,3138852 96,35 102,033431 118,1743476
108,1523649 96,6 109,3138852 102,033431
121,30381 96,62 108,1523649 109,3138852
103,8725146 97,6 121,30381 108,15236 etc... | | Output produced by software: | Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!
Multiple Linear Regression - Estimated Regression Equation | LKI[t] = -17.2457869176623 + 0.508784678881672CPI[t] + 0.307209058050048LKI_1[t] + 0.380198032535317LKI_2[t] + 2.62067959823466Q1[t] + 15.0228547971945Q2[t] -6.65102587567938Q3[t] + e[t] |
Multiple Linear Regression - Ordinary Least Squares | Variable | Parameter | S.D. | T-STAT H0: parameter = 0 | 2-tail p-value | 1-tail p-value | (Intercept) | -17.2457869176623 | 6.345783 | -2.7177 | 0.009176 | 0.004588 | CPI | 0.508784678881672 | 0.205988 | 2.47 | 0.017199 | 0.008599 | LKI_1 | 0.307209058050048 | 0.139895 | 2.196 | 0.033062 | 0.016531 | LKI_2 | 0.380198032535317 | 0.142802 | 2.6624 | 0.010588 | 0.005294 | Q1 | 2.62067959823466 | 3.214801 | 0.8152 | 0.419074 | 0.209537 | Q2 | 15.0228547971945 | 2.123296 | 7.0753 | 0 | 0 | Q3 | -6.65102587567938 | 3.999931 | -1.6628 | 0.103011 | 0.051506 |
Multiple Linear Regression - Regression Statistics | Multiple R | 0.994087973665726 | R-squared | 0.98821089938683 | Adjusted R-squared | 0.98670590781919 | F-TEST (value) | 656.622216785721 | F-TEST (DF numerator) | 6 | F-TEST (DF denominator) | 47 | p-value | 0 | Multiple Linear Regression - Residual Statistics | Residual Standard Deviation | 1.6184093348068 | Sum Squared Residuals | 123.10469242452 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error | 1 | 90.09 | 86.9410862962569 | 3.1489137037431 | 2 | 100.639 | 102.309305704667 | -1.67030570466656 | 3 | 83.042 | 85.2874029209173 | -2.24540292091734 | 4 | 89.956 | 90.6194977491373 | -0.663497749137338 | 5 | 89.561 | 88.9842346503239 | 0.576765349676142 | 6 | 105.38 | 104.071826105912 | 1.30817389408831 | 7 | 86.554 | 87.387338872865 | -0.833338872865004 | 8 | 93.131 | 94.1776184561717 | -1.04661845617166 | 9 | 92.812 | 92.0885829989522 | 0.723417001047783 | 10 | 102.195 | 106.852618194068 | -4.65761819406831 | 11 | 88.925 | 87.9705240212322 | 0.954475978767807 | 12 | 94.184 | 94.427730336773 | -0.243730336772957 | 13 | 94.196 | 93.6544094070709 | 0.541590592929128 | 14 | 108.932 | 107.978327019209 | 0.95367298079056 | 15 | 91.134 | 91.044643120493 | 0.0893568795069851 | 16 | 97.149 | 98.0900405746677 | -0.941040574667734 | 17 | 96.415 | 95.8223451547427 | 0.59265484525726 | 18 | 112.432 | 110.509785329502 | 1.9222146704983 | 19 | 92.47 | 93.8691709862734 | -1.39917098627343 | 20 | 98.61410515 | 100.894524968959 | -2.2804198189589 | 21 | 97.80117197 | 98.1642776258439 | -0.363105655843864 | 22 | 111.8560178 | 112.810412328492 | -0.954394528491658 | 23 | 95.63981455 | 95.2419010974596 | 0.397913452540391 | 24 | 104.1120262 | 103.033227738401 | 1.07879846159948 | 25 | 104.0148224 | 102.254090023632 | 1.76073237636836 | 26 | 118.1743476 | 117.867872922463 | 0.306474677537374 | 27 | 102.033431 | 100.842767843264 | 1.19066315673635 | 28 | 109.3138852 | 108.050865573376 | 1.26301962662388 | 29 | 108.1523649 | 106.898618083653 | 1.25374681634693 | 30 | 121.30381 | 121.722153781725 | -0.418343781724991 | 31 | 103.8725146 | 104.145517422513 | -0.273002822513262 | 32 | 112.7185207 | 110.477259937304 | 2.24126076269553 | 33 | 109.0381253 | 109.473087941577 | -0.434962641576914 | 34 | 122.4434864 | 124.138373532199 | -1.69488713219932 | 35 | 106.6325686 | 105.488732932974 | 1.14383566702581 | 36 | 113.8153852 | 112.882890392147 | 0.932494807852829 | 37 | 111.1071252 | 111.973660198673 | -0.86653499867255 | 38 | 130.039536 | 126.473152158224 | 3.56638384177639 | 39 | 109.6121057 | 109.936865878669 | -0.324760178668566 | 40 | 116.8592117 | 118.065040770793 | -1.20582907079335 | 41 | 113.8982545 | 115.649324999157 | -1.7510704991574 | 42 | 128.9375926 | 129.841236453877 | -0.903643853876615 | 43 | 111.8120023 | 111.814461974203 | -0.00245967420316595 | 44 | 119.9689463 | 119.370008658938 | 0.598937641062172 | 45 | 117.018539 | 118.224588400988 | -1.20604940098795 | 46 | 132.4743387 | 132.745308110919 | -0.270969410919278 | 47 | 116.3369106 | 115.008208708773 | 1.32870189122736 | 48 | 124.6405636 | 122.878118096241 | 1.76244550375922 | 49 | 121.025249 | 122.092411334788 | -1.06716233478832 | 50 | 137.2054829 | 137.227820990874 | -0.0223380908737981 | 51 | 120.0187687 | 120.045580270364 | -0.0268115703639352 | 52 | 127.0443429 | 128.540163697091 | -1.49582079709117 | 53 | 124.349043 | 127.257978154342 | -2.9089351543417 | 54 | 143.6114438 | 141.07586316787 | 2.53558063212959 |
Goldfeld-Quandt test for Heteroskedasticity | p-values | Alternative Hypothesis | breakpoint index | greater | 2-sided | less | 10 | 0.933384986035624 | 0.133230027928751 | 0.0666150139643757 | 11 | 0.921937795008702 | 0.156124409982596 | 0.0780622049912979 | 12 | 0.8652175594991 | 0.269564881001798 | 0.134782440500899 | 13 | 0.80898062662899 | 0.38203874674202 | 0.19101937337101 | 14 | 0.89955214543752 | 0.200895709124959 | 0.10044785456248 | 15 | 0.888602584123432 | 0.222794831753135 | 0.111397415876568 | 16 | 0.847095521581233 | 0.305808956837533 | 0.152904478418767 | 17 | 0.789026682397122 | 0.421946635205755 | 0.210973317602878 | 18 | 0.830802905760954 | 0.338394188478092 | 0.169197094239046 | 19 | 0.812034140704234 | 0.375931718591532 | 0.187965859295766 | 20 | 0.860568455967124 | 0.278863088065753 | 0.139431544032876 | 21 | 0.841697032723562 | 0.316605934552876 | 0.158302967276438 | 22 | 0.837078481417407 | 0.325843037165186 | 0.162921518582593 | 23 | 0.79618005485147 | 0.407639890297058 | 0.203819945148529 | 24 | 0.768775960212639 | 0.462448079574723 | 0.231224039787361 | 25 | 0.735631566160567 | 0.528736867678866 | 0.264368433839433 | 26 | 0.693519233698767 | 0.612961532602466 | 0.306480766301233 | 27 | 0.645035118551283 | 0.709929762897434 | 0.354964881448717 | 28 | 0.601016217143676 | 0.797967565712648 | 0.398983782856324 | 29 | 0.607052526231926 | 0.785894947536148 | 0.392947473768074 | 30 | 0.581889595697985 | 0.83622080860403 | 0.418110404302015 | 31 | 0.495628456069441 | 0.991256912138883 | 0.504371543930559 | 32 | 0.531717608047371 | 0.936564783905259 | 0.468282391952629 | 33 | 0.491987577255809 | 0.983975154511618 | 0.508012422744191 | 34 | 0.631981623668348 | 0.736036752663304 | 0.368018376331652 | 35 | 0.575913418973918 | 0.848173162052164 | 0.424086581026082 | 36 | 0.475195813374315 | 0.95039162674863 | 0.524804186625685 | 37 | 0.440413487552873 | 0.880826975105745 | 0.559586512447127 | 38 | 0.726716810300376 | 0.546566379399249 | 0.273283189699624 | 39 | 0.79119097596576 | 0.417618048068481 | 0.20880902403424 | 40 | 0.729987685219669 | 0.540024629560663 | 0.270012314780331 | 41 | 0.70402412332244 | 0.591951753355121 | 0.295975876677561 | 42 | 0.702013328816525 | 0.595973342366951 | 0.297986671183475 | 43 | 0.554485838748145 | 0.89102832250371 | 0.445514161251855 | 44 | 0.427818457613053 | 0.855636915226106 | 0.572181542386947 |
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity | Description | # significant tests | % significant tests | OK/NOK | 1% type I error level | 0 | 0 | OK | 5% type I error level | 0 | 0 | OK | 10% type I error level | 0 | 0 | OK |
| | Charts produced by software: | | http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/104smi1296051296.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/104smi1296051296.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/1vskw1296051296.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/1vskw1296051296.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/2k0h61296051296.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/2k0h61296051296.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/3829z1296051296.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/3829z1296051296.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/405nz1296051296.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/405nz1296051296.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/52r1x1296051296.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/52r1x1296051296.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/69tob1296051296.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/69tob1296051296.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/7i18s1296051296.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/7i18s1296051296.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/8wr9x1296051296.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/8wr9x1296051296.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/90baf1296051296.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/26/t1296051400oexe56v5qx3smxq/90baf1296051296.ps (open in new window) |
| | Parameters (Session): | par1 = 1 ; par2 = Include Quarterly Dummies ; par3 = No Linear Trend ; | | Parameters (R input): | par1 = 1 ; par2 = Include Quarterly Dummies ; par3 = No Linear Trend ; | | R code (references can be found in the software module): | 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)
a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}
| |
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