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*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: Sat, 18 Dec 2010 13:17:40 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx.htm/, Retrieved Sat, 18 Dec 2010 14:15:50 +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/2010/Dec/18/t1292678139wun7ngsr7a0fvpx.htm/},
    year = {2010},
}
@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 = {2010},
    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 «
31514 -9 0 27071 -13 4 29462 -18 5 26105 -11 -7 22397 -9 -2 23843 -10 1 21705 -13 3 18089 -11 -2 20764 -5 -6 25316 -15 10 17704 -6 -9 15548 -6 0 28029 -3 -3 29383 -1 -2 36438 -3 2 32034 -4 1 22679 -6 2 24319 0 -6 18004 -4 4 17537 -2 -2 20366 -2 0 22782 -6 4 19169 -7 1 13807 -6 -1 29743 -6 0 25591 -3 -3 29096 -2 -1 26482 -5 3 22405 -11 6 27044 -11 0 17970 -11 0 18730 -10 -1 19684 -14 4 19785 -8 -6 18479 -9 1 10698 -5 -4 31956 -1 -4 29506 -2 1 34506 -5 3 27165 -4 -1 26736 -6 2 23691 -2 -4 18157 -2 0 17328 -2 0 18205 -2 0 20995 2 -4 17382 1 1 9367 -8 9 31124 -1 -7 26551 1 -2 30651 -1 2 25859 2 -3 25100 2 0 25778 1 1 20418 -1 2 18688 -2 1 20424 -2 0 24776 -1 -1 19814 -8 7 12738 -4 -4 31566 -6 2 30111 -3 -3 30019 -3 0 31934 -7 4 25826 -9 2 26835 -11 2 20205 -13 2 17789 -11 -2 20520 -9 -2 22518 -17 8 15572 -22 5 11509 -25 3 25447 -20 -5 24090 -24 4 27786 -24 0 26195 -22 -2 20516 -19 -3 22759 -18 -1 19028 -17 -1 16971 -11 -6 20036 -11 0 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!


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ 72.249.76.132


Multiple Linear Regression - Estimated Regression Equation
Inschrijvingen[t] = + 13517.2332809912 + 113.728221602631Consumentenvertrouwen[t] + 178.951790455348Evolutie_consumentenvertrouwen[t] + 17576.0065235033M1[t] + 14711.2983993764M2[t] + 18248.2396782571M3[t] + 15406.8950410103M4[t] + 10911.7059075466M5[t] + 12385.8812668547M6[t] + 6573.46723518118M7[t] + 5461.63876815061M8[t] + 7315.99202385729M9[t] + 9767.35059699243M10[t] + 5492.99734128576M11[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)13517.2332809912770.19390417.550400
Consumentenvertrouwen113.72822160263132.4344813.50640.0007970.000399
Evolutie_consumentenvertrouwen178.95179045534862.772672.85080.0057260.002863
M117576.00652350331022.26027117.193300
M214711.29839937641005.10481914.636600
M318248.23967825711000.67962918.235800
M415406.89504101031005.73511415.31900
M510911.7059075466999.28855910.919500
M612385.88126685471007.57556112.292800
M76573.46723518118999.0963786.579400
M85461.638768150611013.9189545.38671e-060
M97315.992023857291006.8297147.266400
M109767.350596992431000.7923799.759600
M115492.99734128576999.0468765.49821e-060


Multiple Linear Regression - Regression Statistics
Multiple R0.953797765402014
R-squared0.909730177285874
Adjusted R-squared0.892965781638965
F-TEST (value)54.2656112660767
F-TEST (DF numerator)13
F-TEST (DF denominator)70
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1867.21753478605
Sum Squared Residuals244055092.554873


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
13151430069.68581007081444.31418992917
22707127465.8719613549-394.871961354866
32946230613.1239226777-1151.12392267773
42610526420.4553511852-315.455351185195
52239723047.4816132035-650.481613203507
62384324944.784122275-1101.78412227498
72170519149.08900670432555.91099329573
81808917369.9580306022719.041969397763
92076419190.87345410331573.12654589670
102531623368.17845849771947.8215415023
111770416717.2951785631986.704821436906
121554812834.86395137552713.13604862453
132802930215.1997683206-2186.19976832065
142938327756.89987785431626.10012214565
153643831782.19187535124655.80812464884
163203428648.16722604643385.83277395362
172267924104.4734398328-1425.47343983279
182431924829.4038051139-510.403805113859
191800420351.5947915833-2347.59479158329
201753718393.5120250259-856.51202502591
212036620605.7688616433-239.768861643277
222278223318.0217101893-536.021710189285
231916918393.0848615139775.915138486056
241380712655.91216092011151.08783907988
252974330410.8704748788-667.87047487878
262559127350.4916441937-1759.49164419374
272909631359.0647255878-2263.06472558775
282648228892.3425853544-2410.34258535445
292240524251.639493641-1846.63949364103
302704424652.1041102172391.89588978299
311797018839.6900785435-869.69007854349
321873017662.63804266021067.36195733979
331968419956.8373642331-272.837364233097
341978521301.0473624306-1516.04736243055
351847918165.6284183087313.371581691315
361069812232.7850111567-1534.78501115670
373195630263.70442107051692.29557892945
382950628180.02702761781325.97297238224
393450631733.68722260122772.31277739875
402716528290.2636451357-1125.26364513569
412673624104.47343983282631.52656016721
422369124959.8509428193-1268.85094281929
431815719863.2440729672-1706.24407296717
441732818751.4156059366-1423.41560593661
451820520605.7688616433-2400.76886164328
462099522796.2331593676-1801.23315936755
471738219302.910634335-1920.91063433499
48936714217.9736222683-4850.97362226833
493112429726.84904970451397.15095029550
502655127984.3563210596-1433.35632105961
513065132009.6483185564-1358.64831855642
522585928614.7293938408-2755.72939384078
532510024656.3956317431443.604368256863
542577826195.7945599039-417.794559903919
552041820334.875875480583.1241245195095
561868818930.3673963920-242.367396391952
572042420605.7688616433-181.768861643277
582477622991.90386592571784.09613407430
591981419353.0673826434460.9326173566
601273812346.5132327593391.486767240661
613156630768.7740557895797.225944210524
623011127350.49164419372760.50835580626
633001931424.2882944405-1405.28829444047
643193428843.83793260453090.16206739547
652582623763.28877502492062.71122497510
662683525010.00769112771824.9923088723
672020518970.13721624891234.86278375108
681778917369.9580306022419.041969397765
692052019451.76772951421068.23227048584
702251822782.8184343817-264.818434381741
711557217402.9686992959-1830.96869929587
721150911210.8831122915298.116887708478
732544727923.9164201652-2476.91642016522
742409026214.8615237259-2124.86152372593
752778629035.9956407852-1249.99564078522
762619526064.203865833130.796134167013
772051621731.2476067219-1215.24760672185
782275923677.0547685432-918.054768543242
791902817978.36895847241049.63104152764
801697116654.1508687808316.849131219155
812003619582.2148672196453.785132780399
822248522098.7970092075386.202990792539
831873017515.044825341214.95517465999
841453812706.06890922851831.93109077148


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.8348549618493830.3302900763012340.165145038150617
180.72482480348350.5503503930330.2751751965165
190.9604964227877790.07900715442444240.0395035772122212
200.947741612456160.1045167750876780.0522583875438392
210.926372190678120.1472556186437600.0736278093218798
220.914892671171010.1702146576579810.0851073288289905
230.8719148839259580.2561702321480830.128085116074042
240.8371400894954990.3257198210090020.162859910504501
250.7759488449163620.4481023101672760.224051155083638
260.7651656165890170.4696687668219650.234834383410982
270.8207316372870740.3585367254258520.179268362712926
280.8757378588754440.2485242822491120.124262141124556
290.8598802652515640.2802394694968730.140119734748436
300.8740728944433020.2518542111133950.125927105556698
310.8367630694933580.3264738610132840.163236930506642
320.7940604226172850.4118791547654310.205939577382715
330.7457373821717690.5085252356564630.254262617828231
340.7130442008676370.5739115982647250.286955799132363
350.6517240925305370.6965518149389270.348275907469463
360.6578978930887730.6842042138224550.342102106911227
370.6475418435921340.7049163128157320.352458156407866
380.6053975897335170.7892048205329670.394602410266483
390.6790986115062790.6418027769874430.320901388493721
400.6308509363296310.7382981273407380.369149063670369
410.6938641690411930.6122716619176140.306135830958807
420.6519873337687980.6960253324624040.348012666231202
430.6356460596122160.7287078807755680.364353940387784
440.5982134354493320.8035731291013350.401786564550668
450.6296977127640660.7406045744718680.370302287235934
460.6285627873209780.7428744253580430.371437212679021
470.628273019977090.743453960045820.37172698002291
480.9254554464025390.1490891071949220.074544553597461
490.917090580266790.1658188394664210.0829094197332105
500.910160837492920.1796783250141590.0898391625070795
510.8822553655893270.2354892688213470.117744634410673
520.9787056963544510.0425886072910970.0212943036455485
530.9718684428087380.05626311438252330.0281315571912617
540.973907104067760.05218579186447790.0260928959322389
550.9819356718925120.03612865621497540.0180643281074877
560.9816698263209240.03666034735815140.0183301736790757
570.9834848442320760.03303031153584770.0165151557679239
580.9733828041890160.05323439162196720.0266171958109836
590.9571707240484170.08565855190316610.0428292759515830
600.9778645990199530.04427080196009290.0221354009800465
610.961856040679120.07628791864176140.0381439593208807
620.960021603372590.07995679325482080.0399783966274104
630.9992758351687250.001448329662549730.000724164831274863
640.9985441445852860.00291171082942730.00145585541471365
650.9963546266278120.0072907467443750.0036453733721875
660.9982068124806540.003586375038691470.00179318751934573
670.9922676391713250.01546472165735100.00773236082867552


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0784313725490196NOK
5% type I error level100.196078431372549NOK
10% type I error level170.333333333333333NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/109jgf1292678252.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/109jgf1292678252.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/1d9j61292678252.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/1d9j61292678252.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/2d9j61292678252.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/2d9j61292678252.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/3d9j61292678252.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/3d9j61292678252.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/46i0r1292678252.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/46i0r1292678252.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/56i0r1292678252.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/56i0r1292678252.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/66i0r1292678252.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/66i0r1292678252.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/7hrhc1292678252.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/7hrhc1292678252.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/89jgf1292678252.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/89jgf1292678252.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/99jgf1292678252.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292678139wun7ngsr7a0fvpx/99jgf1292678252.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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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Software written by Ed van Stee & Patrick Wessa


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