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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: Sun, 22 Nov 2009 07:13:11 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9.htm/, Retrieved Sun, 22 Nov 2009 15:33:25 +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/2009/Nov/22/t12589003933lwvnkfiy7actt9.htm/},
    year = {2009},
}
@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 = {2009},
    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 «

10414.9 10723.8 12476.8 13938.9 12384.6 13979.8 12266.7 13807.4 12919.9 12973.9 11497.3 12509.8 12142 12934.1 13919.4 14908.3 12656.8 13772.1 12034.1 13012.6 13199.7 14049.9 10881.3 11816.5 11301.2 11593.2 13643.9 14466.2 12517 13615.9 13981.1 14733.9 14275.7 13880.7 13435 13527.5 13565.7 13584 16216.3 16170.2 12970 13260.6 14079.9 14741.9 14235 15486.5 12213.4 13154.5 12581 12621.2 14130.4 15031.6 14210.8 15452.4 14378.5 15428 13142.8 13105.9 13714.7 14716.8 13621.9 14180 15379.8 16202.2 13306.3 14392.4 14391.2 15140.6 14909.9 15960.1 14025.4 14351.3 12951.2 13230.2 14344.3 15202.1 16093.4 17056 15413.6 16077.7 14705.7 13348.2 15972.8 16402.4 16241.4 16559.1 16626.4 16579 17136.2 17561.2 15622.9 16129.6 18003.9 18484.3 16136.1 16402.6 14423.7 14032.3 16789.4 17109.1 16782.2 17157.2 14133.8 13879.8 12607 12362.4 12004.5 12683.5 12175.4 12608.8 13268 13583.7 12299.3 12846.3 11800.6 12347.1 13873.3 13967 12269.6 13114.3

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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

In_IEU[t] = -1635.22504557635 + 1.03943856780702Uit_IEU[t] + 740.557383587982M1[t] -145.111303853829M2[t] -351.020823840440M3[t] -32.611497252725M4[t] + 1167.19484531337M5[t] + 83.2395019644493M6[t] + 290.322167129069M7[t] + 235.841552106109M8[t] -17.9343189263537M9[t] -22.0519148000427M10[t] -142.433773462613M11[t] + 11.9322542827837t + 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)	-1635.22504557635	504.143869	-3.2436	0.002201	0.001101
Uit_IEU	1.03943856780702	0.03671	28.3146	0	0
M1	740.557383587982	227.168341	3.2599	0.002101	0.00105
M2	-145.111303853829	231.786449	-0.6261	0.534373	0.267186
M3	-351.020823840440	234.349808	-1.4978	0.141004	0.070502
M4	-32.611497252725	227.675438	-0.1432	0.886729	0.443364
M5	1167.19484531337	223.458312	5.2233	4e-06	2e-06
M6	83.2395019644493	223.060105	0.3732	0.710736	0.355368
M7	290.322167129069	222.836866	1.3028	0.199112	0.099556
M8	235.841552106109	232.267613	1.0154	0.315232	0.157616
M9	-17.9343189263537	223.626566	-0.0802	0.936428	0.468214
M10	-22.0519148000427	223.09909	-0.0988	0.921692	0.460846
M11	-142.433773462613	232.23903	-0.6133	0.542694	0.271347
t	11.9322542827837	2.853347	4.1818	0.000128	6.4e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.982637569441814
R-squared	0.965576592878516
Adjusted R-squared	0.95584823869201
F-TEST (value)	99.253848530496
F-TEST (DF numerator)	13
F-TEST (DF denominator)	46
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	351.141979660972
Sum Squared Residuals	5671831.73449042

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	10414.9	10263.9959057434	150.904094256648
2	12476.8	12732.1584119406	-255.358411940591
3	12384.6	12580.6941836601	-196.094183660072
4	12266.7	12731.8365554406	-465.136555440641
5	12919.9	13077.2031060224	-157.303106022370
6	11497.3	11522.776577637	-25.4765776370010
7	12142	12182.8252814049	-40.8252814049234
8	13919.4	14192.3365412294	-272.936541229355
9	12656.8	12769.4828237373	-112.682823737346
10	12034.1	11987.843889897	46.2561101029888
11	13199.7	12957.6039119034	242.096088096559
12	10881.3	10790.4878423087	90.8121576913492
13	11301.2	11310.8708479881	-9.67084798810856
14	13643.9	13423.4414201386	220.458579861361
15	12517	12345.6295402285	171.370459771496
16	13981.1	13838.0634399072	143.036560092754
17	14275.7	14162.9530507032	112.746949296823
18	13435	12723.8002594876	711.199740512395
19	13565.7	13001.5434580161	564.156541983896
20	16216.3	15647.1911213384	569.108878661566
21	12970	12380.9970476975	589.002952302539
22	14079.9	13928.5320565991	151.367943400912
23	14235	14594.0484098084	-359.048409808405
24	12213.4	12324.4436974278	-111.043697427842
25	12581	12522.6007470871	58.3992529128742
26	14130.4	14154.3270377701	-23.9270377701293
27	14210.8	14397.7455213995	-186.945521399493
28	14378.5	14702.7248012155	-324.224801215501
29	13142.8	13500.7830997597	-357.983099759706
30	13714.7	14103.1915995739	-388.491599573892
31	13621.9	13764.2358958225	-142.335895822491
32	15379.8	15823.6402069017	-443.840206901663
33	13306.3	13700.6206701348	-394.320670134846
34	14391.2	14486.1432649771	-94.9432649771486
35	14909.9	15229.5135669152	-319.613566915213
36	14025.4	13711.6308267727	313.769173227319
37	12951.2	13298.805886275	-347.605886275002
38	14344.3	14474.7383649746	-130.438364974631
39	16093.4	16207.7762601282	-114.376260128228
40	15413.6	15521.2350901131	-107.635090113123
41	14705.7	13895.8261161328	809.87388386725
42	15972.8	15998.4563008628	-25.6563008628048
43	16241.4	16380.3512438856	-138.951243885565
44	16626.4	16358.4877106447	267.912289355252
45	17136.2	17137.5806551951	-1.38065519512051
46	15622.9	15657.3350599317	-34.4350599316925
47	18003.9	17996.4514511671	7.44854883291681
48	16136.1	15987.0182123086	149.081787691385
49	14423.7	14275.7266129064	147.973387093588
50	16789.4	16600.134765176	189.265234823990
51	16782.2	16456.1544945837	326.045505416298
52	14133.8	13379.8401133235	753.959886676512
53	12607	13014.334627382	-407.334627381998
54	12004.5	12276.0752624387	-271.575262438697
55	12175.4	12417.4441208709	-242.044120870916
56	13268	13388.2444198858	-120.244419885800
57	12299.3	12379.9188032352	-80.6188032352273
58	11800.6	11868.8457285951	-68.2457285950599
59	13873.3	13444.1826602059	429.117339794142
60	12269.6	12712.2194211822	-442.619421182211

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.195657384781905	0.39131476956381	0.804342615218095
18	0.194063652900467	0.388127305800934	0.805936347099533
19	0.138820903247369	0.277641806494739	0.86117909675263
20	0.134119372613347	0.268238745226695	0.865880627386653
21	0.159971493769724	0.319942987539447	0.840028506230276
22	0.143590384638628	0.287180769277256	0.856409615361372
23	0.38308469437813	0.76616938875626	0.61691530562187
24	0.341248670057270	0.682497340114541	0.65875132994273
25	0.330300072913153	0.660600145826306	0.669699927086847
26	0.328462386183632	0.656924772367265	0.671537613816368
27	0.277700562632804	0.555401125265608	0.722299437367196
28	0.280698498516566	0.561396997033133	0.719301501483434
29	0.385020167831237	0.770040335662474	0.614979832168763
30	0.445591669561847	0.891183339123694	0.554408330438153
31	0.408333409893542	0.816666819787084	0.591666590106458
32	0.432566886641347	0.865133773282694	0.567433113358653
33	0.407703147953962	0.815406295907923	0.592296852046039
34	0.311466602406775	0.62293320481355	0.688533397593225
35	0.281529208925604	0.563058417851208	0.718470791074396
36	0.288603994779372	0.577207989558745	0.711396005220628
37	0.238224507249945	0.476449014499889	0.761775492750055
38	0.176788364493332	0.353576728986664	0.823211635506668
39	0.242091349941485	0.48418269988297	0.757908650058515
40	0.97853614576133	0.0429277084773388	0.0214638542386694
41	0.96373959150614	0.0725208169877183	0.0362604084938592
42	0.911159494011774	0.177681011976453	0.0888405059882265
43	0.799261300526034	0.401477398947932	0.200738699473966

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	1	0.0370370370370370	OK
10% type I error level	2	0.0740740740740741	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/10m7nd1258899186.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/10m7nd1258899186.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/1nen51258899186.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/1nen51258899186.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/269bv1258899186.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/269bv1258899186.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/3hlf41258899186.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/3hlf41258899186.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/4qszc1258899186.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/4qszc1258899186.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/5vqil1258899186.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/5vqil1258899186.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/6a5mi1258899186.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/6a5mi1258899186.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/765h71258899186.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/765h71258899186.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/8scdj1258899186.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/8scdj1258899186.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/9bmhq1258899186.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589003933lwvnkfiy7actt9/9bmhq1258899186.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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