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R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 20 Nov 2009 09:38:23 -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/20/t1258735192xr6atmcae1bragb.htm/, Retrieved Fri, 20 Nov 2009 17:40:04 +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/20/t1258735192xr6atmcae1bragb.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 «

100 100 97.82226485 99.87129987 94.04971502 99.54459954 91.12460521 99.81189981 93.13202153 100.4851005 93.88342812 101.1385011 92.55349954 101.3662014 94.43494835 101.5147015 96.25017563 101.8216018 100.4355715 102.4354024 101.5036685 102.5344025 99.39789728 102.6532027 99.68990733 102.4651025 101.6895041 102.4354024 103.6652759 102.4156024 103.0532766 102.4453024 100.9500712 102.8908029 102.345366 102.8512029 101.6472299 103.3561034 99.56809393 103.7422037 95.67727392 103.7224037 96.58494865 104.0788041 96.32604937 104.2075042 95.37109101 103.9105039 96.00056203 103.7026037 96.88367859 103.960004 94.85280372 104.0986041 92.46943974 104.1481041 93.99180173 104.7124047 93.45262168 104.7223047 92.26698759 105.1975052 90.39653498 105.0688051 90.43001228 105.0589051 91.04995327 105.5044055 89.07845784 105.3757054 89.69314509 105.4747055 87.92459054 106.029106 85.8789319 107.019107 83.20612366 107.3161073 83.85722053 107.7517078 83.01393462 108.5239085 82.84508195 109.3159 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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

wisselkoers[t] = + 349.988239271188 -2.52476554136672consumptieprijzen[t] + 1.29489382228422M1[t] + 2.31327863793316M2[t] + 1.52204502734436M3[t] -0.371286169745872M4[t] + 1.19713341446275M5[t] + 2.26907632053482M6[t] + 0.990607404803453M7[t] + 0.5481196762725M8[t] -0.212386393374486M9[t] + 1.17543883368711M10[t] + 0.246538544527837M11[t] + 0.266161533870994t + 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)	349.988239271188	69.001185	5.0722	7e-06	3e-06
consumptieprijzen	-2.52476554136672	0.694008	-3.6379	0.000693	0.000346
M1	1.29489382228422	3.366384	0.3847	0.702266	0.351133
M2	2.31327863793316	3.358342	0.6888	0.494399	0.247199
M3	1.52204502734436	3.353631	0.4538	0.652071	0.326035
M4	-0.371286169745872	3.349987	-0.1108	0.912232	0.456116
M5	1.19713341446275	3.348802	0.3575	0.722367	0.361183
M6	2.26907632053482	3.344319	0.6785	0.500862	0.250431
M7	0.990607404803453	3.346479	0.296	0.768551	0.384276
M8	0.5481196762725	3.347239	0.1638	0.870643	0.435322
M9	-0.212386393374486	3.342312	-0.0635	0.949608	0.474804
M10	1.17543883368711	3.351688	0.3507	0.727414	0.363707
M11	0.246538544527837	3.337674	0.0739	0.941438	0.470719
t	0.266161533870994	0.146192	1.8206	0.075172	0.037586

Multiple Linear Regression - Regression Statistics
Multiple R	0.742032416370845
R-squared	0.550612106945155
Adjusted R-squared	0.423611180647047
F-TEST (value)	4.33549677939125
F-TEST (DF numerator)	13
F-TEST (DF denominator)	46
p-value	0.000102863886039639
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.27035806442279
Sum Squared Residuals	1277.72700985241

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	100	99.072740490672	0.927259509328086
2	97.82226485	100.682224493585	-2.85995964358523
3	94.04971502	100.981994152405	-6.93227913240457
4	91.12460521	98.6799539782913	-7.55534876829128
5	93.13202153	98.8148611918346	-5.68283966183461
6	93.88342812	98.5032823121893	-4.61985419218933
7	92.55349954	96.9160850591301	-4.36258551913011
8	94.43494835	96.3648309291006	-1.92988257910063
9	96.25017563	95.0956350912495	1.15454053875047
10	100.4355715	95.199919248032	5.2356522519681
11	101.5036685	94.2872284516718	7.21644004832823
12	99.39789728	94.0069087897475	5.39098849025254
13	99.68990733	96.0428730491868	3.64703428081316
14	101.6895041	97.402405187762	4.28709891223807
15	103.6652759	96.9273234687632	6.73795243123682
16	103.0532766	95.2251682689653	7.82810833103466
17	100.9500712	95.9349650759833	5.01510612401666
18	102.345366	97.3730502313645	4.9723157686355
19	101.6472299	95.0859874652853	6.56124243471468
20	99.56809393	93.934848537674	5.63324539232602
21	95.67727392	93.490494359617	2.18677956038294
22	96.58494865	94.2446536717003	2.34029497829966
23	96.32604937	93.2569773387616	3.0690720312384
24	95.37109101	94.0264564513204	1.34463455867964
25	96.00056203	96.1124110684788	-0.111849038478814
26	96.88367859	96.7470820102213	0.136596579778710
27	94.85280372	95.8720771769935	-1.01927345699349
28	92.46943974	94.1199316194766	-1.65049187947662
29	93.99180173	94.5297860277037	-0.537984297703675
30	93.45262168	95.8428952887872	-2.39027360878722
31	92.26698759	93.6308180592866	-1.36383046928660
32	90.39653498	93.779429442277	-3.38289446227706
33	90.43001228	93.3100800853606	-2.88006780536060
34	91.04995327	93.8392827877081	-2.78932951770810
35	89.07845784	93.5014816100703	-4.42302377007029
36	89.69314509	93.2711525583416	-3.57800746834159
37	87.92459054	93.4324766359803	-5.50788609598033
38	85.8789319	92.2175025747816	-6.33857067478166
39	83.20612366	90.9425743748483	-7.73645071484828
40	83.85722053	88.2156155794269	-4.35839504942691
41	83.01393462	88.1005709791273	-5.08663635912728
42	82.84508195	87.4390590904955	-4.59397714049549
43	78.68864276	85.8018714797172	-7.11322871971719
44	77.56959675	83.1510203056747	-5.58142355567472
45	78.53689529	81.106972913272	-2.57007762327196
46	78.55717715	81.2862428591095	-2.72906570910947
47	77.4761291	82.3481729599881	-4.87204385998813
48	81.58931659	81.9178824773832	-0.328565887383194
49	85.02428326	83.9788419156821	1.0454413443179
50	91.71290159	86.9380667636499	4.77483482635009
51	95.96293061	87.0128797369905	8.95005087300952
52	90.84689022	85.1107628538399	5.73612736616014
53	92.28788036	85.9955261653511	6.2923541946489
54	95.56511274	88.9333235671635	6.63178917283654
55	93.62452884	87.3461265665808	6.27840227341922
56	92.63071726	87.3697620552736	5.2609552047264
57	89.50914211	87.4003167805008	2.10882532949915
58	87.17171779	89.2292697934502	-2.05755200345018
59	86.72624975	87.7166941995082	-0.990444449508212
60	85.63212844	88.4611781332074	-2.82904969320738

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.277632309414992	0.555264618829984	0.722367690585008
18	0.237550774907982	0.475101549815964	0.762449225092018
19	0.156906228274629	0.313812456549258	0.843093771725371
20	0.106631230423521	0.213262460847041	0.89336876957648
21	0.135190253883415	0.270380507766829	0.864809746116585
22	0.179066659397847	0.358133318795694	0.820933340602153
23	0.251688495113011	0.503376990226023	0.748311504886989
24	0.233222769500974	0.466445539001947	0.766777230499026
25	0.203229066420603	0.406458132841205	0.796770933579397
26	0.190434145635742	0.380868291271483	0.809565854364258
27	0.238615328714949	0.477230657429899	0.76138467128505
28	0.237434077181306	0.474868154362612	0.762565922818694
29	0.203051205214658	0.406102410429315	0.796948794785342
30	0.148611967039167	0.297223934078333	0.851388032960833
31	0.125792877441967	0.251585754883934	0.874207122558033
32	0.0861037319441527	0.172207463888305	0.913896268055847
33	0.0613247907320256	0.122649581464051	0.938675209267974
34	0.0617795350553275	0.123559070110655	0.938220464944673
35	0.0842955926177929	0.168591185235586	0.915704407382207
36	0.342343029013193	0.684686058026387	0.657656970986807
37	0.77256394904283	0.45487210191434	0.22743605095717
38	0.934422198310935	0.131155603378131	0.0655778016890653
39	0.936747253377237	0.126505493245526	0.063252746622763
40	0.939988897957376	0.120022204085248	0.0600111020426239
41	0.963645517483246	0.0727089650335073	0.0363544825167537
42	0.929036457537514	0.141927084924972	0.070963542462486
43	0.849291964258967	0.301416071482067	0.150708035741033

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	1	0.0370370370370370	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/10ahat1258735091.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/10ahat1258735091.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/14ygz1258735091.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/14ygz1258735091.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/2p79v1258735091.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/2p79v1258735091.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/3179d1258735091.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/3179d1258735091.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/4y3il1258735091.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/4y3il1258735091.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/5d8dx1258735091.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/5d8dx1258735091.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/68yju1258735091.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/68yju1258735091.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/7nfj41258735091.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/7nfj41258735091.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/848yh1258735091.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/848yh1258735091.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/9kvgo1258735091.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735192xr6atmcae1bragb/9kvgo1258735091.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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