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werkloosheid - Amerika

*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: Fri, 19 Dec 2008 16:13:48 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/20/t1229728633esjbqoz6mirep75.htm/, Retrieved Sat, 20 Dec 2008 00:17:13 +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/2008/Dec/20/t1229728633esjbqoz6mirep75.htm/},
    year = {2008},
}
@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 = {2008},
    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 «
180144 966.2 173666 1153.2 165688 1328.3 161570 1144.5 156145 1477.1 153730 1234.9 182698 1119.1 200765 1356.9 176512 1217 166618 1440.5 158644 1556.6 159585 1303.6 163095 1421.5 159044 1172.5 155511 1422.1 153745 1263 150569 1428.1 150605 1347 179612 1224.2 194690 1201.3 189917 997.8 184128 1248.8 175335 1268.6 179566 1016.7 181140 1194.3 177876 1181.8 175041 1150.7 169292 1247.2 166070 1260.6 166972 1249.3 206348 1223.2 215706 1153 202108 1191.5 195411 1303.1 193111 1267.1 195198 1125.2 198770 1322.4 194163 1089.2 190420 1147.3 189733 1196.4 186029 1190.2 191531 1146 232571 1139.8 243477 1045.6 227247 1050.9 217859 1117.3 208679 1120 213188 1052.1 216234 1065.8 213586 1092.5 209465 1422 204045 1367.5 200237 1136.3 203666 1293.7 241476 1154.8 260307 1206.7 243324 1199 244460 1265 233575 1247.1 237217 1116.5 235243 1153.9 230354 1077.4 227184 1132.5 221678 1058.8 217142 1195.1 219452 1263.4 256446 1023.1 265845 1141 248624 1116.3 241114 1135.6 229245 1210.5 231805 1230 219277 1136.5 219313 1068.7 212610 1372.5 214771 1049.9 211142 1302.2 211457 1305.9 240048 1173.5 240636 1277.4 230580 1238.6 208795 1508.6 197922 1423.4 194596 1375.1
 
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 time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 234322.380521586 -63.9859480837532Amerika[t] + 7630.90751565396M1[t] -840.206708750359M2[t] + 4114.62237117492M3[t] -5705.073172238M4[t] -4463.0278058009M5[t] -5272.18159073272M6[t] + 21232.5926626463M7[t] + 35059.4024509238M8[t] + 16056.0359651716M9[t] + 15823.8163405996M10[t] + 6781.38472440267M11[t] + 883.367978687148t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)234322.38052158615365.74365615.249700
Amerika-63.985948083753212.018629-5.32391e-061e-06
M17630.907515653966248.5731421.22120.2260980.113049
M2-840.2067087503596287.580473-0.13360.8940790.447039
M34114.622371174926358.7407420.64710.5196960.259848
M4-5705.0731722386236.929481-0.91470.3634760.181738
M5-4463.02780580096359.653378-0.70180.4851480.242574
M6-5272.181590732726312.022491-0.83530.4064130.203207
M721232.59266264636235.110583.40530.0010980.000549
M835059.40245092386229.8702995.627600
M916056.03596517166235.0953012.57510.0121360.006068
M1015823.81634059966368.4885622.48470.015360.00768
M116781.384724402676398.1547081.05990.2928350.146418
t883.36797868714853.53367216.501200


Multiple Linear Regression - Regression Statistics
Multiple R0.93181656740291
R-squared0.868282115286542
Adjusted R-squared0.843820222411186
F-TEST (value)35.495295466741
F-TEST (DF numerator)13
F-TEST (DF denominator)70
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11639.1329181000
Sum Squared Residuals9482859055.9639


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
1180144181013.432977405-869.432977404915
2173666161460.31444002612205.6855599742
3165688156094.5719891739593.42801082693
4161570158918.8616822412651.13831775888
5156145139762.54869470916382.4513052909
6153730155334.159514349-1604.15951434941
7182698190131.874534514-7433.87453451421
8200765189626.19384716211138.8061528376
9176512180457.829477014-3945.82947701433
10166618166808.118434411-190.118434410704
11158644151220.2862243777423.71377562284
12159585161510.714343851-1925.7143438512
13163095162481.046559118613.953440882212
14159044170825.801386255-11781.8013862552
15155511160693.105803163-5182.1058031628
16153745161936.942578562-8191.94257856216
17150569153498.275895059-2929.27589505876
18150605158761.750478406-8156.75047840647
19179612194007.367135158-14395.3671351575
20194690210182.82311324-15492.8231132401
21189917205083.965041219-15166.9650412188
22184128189674.640426312-5546.64042631199
23175335180248.655016744-4913.65501674385
24179566190468.698593326-10902.6985933258
25181140187619.069707992-6479.0697079923
26177876180831.147813322-2955.14781332204
27175041188659.307857339-13618.3078573392
28169292173548.336302531-4256.33630253124
29166070174816.337943333-8746.3379433332
30166972175613.593350435-8641.59335043493
31206348204671.7688274871676.23117251296
32215706223873.760149931-8167.7601499312
33202108203290.302641642-1182.30264164161
34195411196800.61918961-1389.61918960997
35193111190945.0496831152165.95031688474
36195198194126.6389704841071.36102951570
37198770190022.8855027098747.11449729072
38194163197356.662350123-3193.66235012336
39190420199477.275825070-9057.27582506974
40189733187399.2382094322333.76179056833
41186029189921.364432675-3892.36443267519
42191531192823.757531732-1292.75753173241
43232571220608.61264191811962.3873580822
44243477241346.2667183722130.73328162793
45227247222887.1426864634359.85731353692
46217859219289.624087817-1430.62408781708
47208679210957.798390481-2278.79839048113
48213188209404.4275196523783.57248034755
49216234217042.095525246-808.095525246138
50213586207745.9244656935840.07553430724
51209465192500.75163070916964.2483692915
52204045187051.65823654716993.3417634527
53200237203970.622778635-3733.62277863527
54203666193973.4487440089692.55125599216
55241476230249.23916490711226.7608350927
56260307241638.54622632518668.4537736748
57243324224011.23951950519312.760480495
58244460220439.31530009324020.6846999075
59233575213425.60013328220149.3998667181
60237217215884.14820730521332.8517926955
61235243222005.34924331313237.6507566868
62230354219312.52802600311041.4719739968
63227184221625.0993452015558.90065479916
64221678217404.5361542484273.46384575233
65217142210808.6647755566333.33522444364
66219452206512.63871519112939.3612848087
67256446249276.6042717837169.39572821663
68265845256442.8387596749402.16124032642
69248624239903.2931702778720.70682972283
70241114239319.5127263761794.48727362404
71229245226367.9015773932877.09842260698
72231805219222.15884404412582.8411559557
73219277233719.120484216-14442.1204842163
74219313230469.621518578-11156.6215185776
75212610216868.887549346-4258.88754934585
76214771228574.426836439-13803.4268364388
77211142214556.185480032-3414.18548003217
78211457214393.651665878-2936.65166587761
79240048250253.533424233-10205.5334242327
80240636258315.571185295-17679.5711852954
81230580242678.22746388-12098.2274638800
82208795226053.169835382-17258.1698353818
83197922223345.708974608-25423.7089746077
84194596220538.213521337-25942.2135213375


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.01427918073953370.02855836147906750.985720819260466
180.01039937648156270.02079875296312550.989600623518437
190.005237593289372930.01047518657874590.994762406710627
200.001496167842391850.002992335684783700.998503832157608
210.006858595487040630.01371719097408130.99314140451296
220.01216761691261770.02433523382523540.987832383087382
230.006597959021760540.01319591804352110.99340204097824
240.004644797084668050.00928959416933610.995355202915332
250.004839121181386930.009678242362773860.995160878818613
260.005130131262110160.01026026252422030.99486986873789
270.002987992414675510.005975984829351020.997012007585324
280.003002234501725150.00600446900345030.996997765498275
290.001490963675997830.002981927351995660.998509036324002
300.001432705856597000.002865411713193990.998567294143403
310.008577616885894850.01715523377178970.991422383114105
320.006782975602438830.01356595120487770.993217024397561
330.009662553274302870.01932510654860570.990337446725697
340.008474835569823130.01694967113964630.991525164430177
350.007351944792808140.01470388958561630.992648055207192
360.009099578986950240.01819915797390050.99090042101305
370.009473910393874070.01894782078774810.990526089606126
380.00724541345808090.01449082691616180.99275458654192
390.008490127899450850.01698025579890170.99150987210055
400.008153678559616040.01630735711923210.991846321440384
410.006935553003451760.01387110600690350.993064446996548
420.01107546978199720.02215093956399440.988924530218003
430.02490061594450300.04980123188900590.975099384055497
440.03105722942044090.06211445884088180.96894277057956
450.04423676981714510.08847353963429020.955763230182855
460.06746096874318290.1349219374863660.932539031256817
470.1081121090475310.2162242180950610.89188789095247
480.2236781448418690.4473562896837370.776321855158131
490.3087824147843890.6175648295687780.691217585215611
500.3382872604821330.6765745209642660.661712739517867
510.3304847655677990.6609695311355990.669515234432201
520.3063301237705470.6126602475410930.693669876229453
530.7292136207165880.5415727585668240.270786379283412
540.889741203256410.2205175934871780.110258796743589
550.9340426391947870.1319147216104250.0659573608052126
560.9326933768710050.134613246257990.067306623128995
570.9484802718642110.1030394562715770.0515197281357886
580.9390081780695930.1219836438608130.0609918219304067
590.913351018498740.1732979630025210.0866489815012606
600.8870498130205860.2259003739588270.112950186979414
610.8480582530167280.3038834939665440.151941746983272
620.772402988322950.4551940233540990.227597011677049
630.7890448550478070.4219102899043870.210955144952193
640.6850918533081520.6298162933836950.314908146691848
650.7289726260150730.5420547479698550.271027373984927
660.7203472627119210.5593054745761580.279652737288079
670.7104040095677090.5791919808645820.289595990432291


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.137254901960784NOK
5% type I error level270.529411764705882NOK
10% type I error level290.568627450980392NOK
 
Charts produced by software:
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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229728633esjbqoz6mirep75/1jq5m1229728413.ps (open in new window)


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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229728633esjbqoz6mirep75/460o41229728413.ps (open in new window)


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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229728633esjbqoz6mirep75/589531229728413.ps (open in new window)


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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229728633esjbqoz6mirep75/6zj661229728413.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229728633esjbqoz6mirep75/7v2up1229728413.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229728633esjbqoz6mirep75/7v2up1229728413.ps (open in new window)


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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229728633esjbqoz6mirep75/8lzm51229728413.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229728633esjbqoz6mirep75/9d3181229728413.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229728633esjbqoz6mirep75/9d3181229728413.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 = 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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