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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: Fri, 20 Nov 2009 04:43:51 -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/t1258717478i102gjsx5cbwr5c.htm/, Retrieved Fri, 20 Nov 2009 12:44:50 +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/2009/Nov/20/t1258717478i102gjsx5cbwr5c.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 «

1.4 2 1.2 2 1 2 1.7 2 2.4 2 2 2 2.1 2 2 2 1.8 2 2.7 2 2.3 2 1.9 2 2 2 2.3 2 2.8 2 2.4 2 2.3 2 2.7 2 2.7 2 2.9 2 3 2 2.2 2 2.3 2 2.8 2.21 2.8 2.25 2.8 2.25 2.2 2.45 2.6 2.5 2.8 2.5 2.5 2.64 2.4 2.75 2.3 2.93 1.9 3 1.7 3.17 2 3.25 2.1 3.39 1.7 3.5 1.8 3.5 1.8 3.65 1.8 3.75 1.3 3.75 1.3 3.9 1.3 4 1.2 4 1.4 4 2.2 4 2.9 4 3.1 4 3.5 4 3.6 4 4.4 4 4.1 4 5.1 4 5.8 4 5.9 4.18 5.4 4.25 5.5 4.25 4.8 3.97 3.2 3.42 2.7 2.75 2.1 2.31 1.9 2 0.6 1.66 0.7 1.31 -0.2 1.09 -1 1 -1.7 1 -0.7 1 -1 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 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

Y[t] = -0.113771342246555 + 0.91769036315211X[t] -0.0925798631239247M1[t] -0.0284991943610653M2[t] -0.163362011632986M3[t] -0.0494389995279161M4[t] + 0.0508763137876616M5[t] -0.0463800316507418M6[t] -0.206029905255629M7[t] -0.177600337053634M8[t] -0.271640057957075M9[t] + 0.055004922621967M10[t] -0.0387321832417344M11[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)	-0.113771342246555	0.716703	-0.1587	0.874443	0.437221
X	0.91769036315211	0.15556	5.8993	0	0
M1	-0.0925798631239247	0.759731	-0.1219	0.903447	0.451724
M2	-0.0284991943610653	0.760091	-0.0375	0.970224	0.485112
M3	-0.163362011632986	0.760078	-0.2149	0.830604	0.415302
M4	-0.0494389995279161	0.760354	-0.065	0.948389	0.474194
M5	0.0508763137876616	0.760698	0.0669	0.946915	0.473457
M6	-0.0463800316507418	0.760384	-0.061	0.95158	0.47579
M7	-0.206029905255629	0.759872	-0.2711	0.787282	0.393641
M8	-0.177600337053634	0.759614	-0.2338	0.81599	0.407995
M9	-0.271640057957075	0.759552	-0.3576	0.721963	0.360982
M10	0.055004922621967	0.793272	0.0693	0.944967	0.472483
M11	-0.0387321832417344	0.792953	-0.0488	0.961216	0.480608

Multiple Linear Regression - Regression Statistics
Multiple R	0.626562034371423
R-squared	0.392579982915657
Adjusted R-squared	0.262418550683297
F-TEST (value)	3.01610066962722
F-TEST (DF numerator)	12
F-TEST (DF denominator)	56
p-value	0.00255174287534687
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.25367055569551
Sum Squared Residuals	88.0146322842021

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1.4	1.62902952093374	-0.229029520933737
2	1.2	1.69311018969660	-0.493110189696596
3	1	1.55824737242468	-0.558247372424678
4	1.7	1.67217038452975	0.0278296154702518
5	2.4	1.77248569784533	0.627514302154674
6	2	1.67522935240692	0.324770647593079
7	2.1	1.51557947880203	0.584420521197966
8	2	1.54400904700403	0.455990952995969
9	1.8	1.44996932610059	0.350030673899411
10	2.7	1.77661430667963	0.92338569332037
11	2.3	1.68287720081593	0.617122799184071
12	1.9	1.72160938405766	0.178390615942336
13	2	1.62902952093374	0.370970479066261
14	2.3	1.6931101896966	0.606889810303401
15	2.8	1.55824737242468	1.24175262757532
16	2.4	1.67217038452975	0.727829615470252
17	2.3	1.77248569784533	0.527514302154674
18	2.7	1.67522935240692	1.02477064759308
19	2.7	1.51557947880203	1.18442052119797
20	2.9	1.54400904700403	1.35599095299597
21	3	1.44996932610059	1.55003067389941
22	2.2	1.77661430667963	0.423385693320369
23	2.3	1.68287720081593	0.61712279918407
24	2.8	1.91432436031961	0.885675639680393
25	2.8	1.85845211172177	0.941547888278233
26	2.8	1.92253278048463	0.877467219515374
27	2.2	1.97120803584313	0.228791964156873
28	2.6	2.1310155661058	0.468984433894197
29	2.8	2.23133087942138	0.56866912057862
30	2.5	2.26255118482427	0.237448815175728
31	2.4	2.20384725116612	0.196152748833883
32	2.3	2.39746108473549	-0.097461084735493
33	1.9	2.3676596892527	-0.467659689252699
34	1.7	2.8503120315676	-1.1503120315676
35	2	2.82999015475607	-0.829990154756067
36	2.1	2.99719898883910	-0.897198988839096
37	1.7	3.00556506566190	-1.30556506566190
38	1.8	3.06964573442476	-1.26964573442476
39	1.8	3.07243647162566	-1.27243647162566
40	1.8	3.27812852004594	-1.47812852004594
41	1.3	3.37844383336152	-2.07844383336152
42	1.3	3.41884104239593	-2.11884104239593
43	1.3	3.35096020510625	-2.05096020510625
44	1.2	3.37938977330825	-2.17938977330825
45	1.4	3.28535005240481	-1.88535005240481
46	2.2	3.61199503298385	-1.41199503298385
47	2.9	3.51825792712015	-0.618257927120149
48	3.1	3.55699011036188	-0.456990110361884
49	3.5	3.46441024723796	0.035589752762041
50	3.6	3.52849091600082	0.0715090839991817
51	4.4	3.3936280987289	1.00637190127110
52	4.1	3.50755111083397	0.592448889166032
53	5.1	3.60786642414955	1.49213357585045
54	5.8	3.51061007871114	2.28938992128886
55	5.9	3.51614447047363	2.38385552952637
56	5.4	3.60881236409628	1.79118763590372
57	5.5	3.51477264319284	1.98522735680716
58	4.8	3.58446432208929	1.21553567791071
59	3.2	2.98599751649193	0.214002483508075
60	2.7	2.40987715642175	0.290122843578254
61	2.1	1.91351353351089	0.186486466489107
62	1.9	1.6931101896966	0.206889810303402
63	0.6	1.24623264895296	-0.64623264895296
64	0.7	1.03896403395479	-0.338964033954792
65	-0.2	0.937387467376906	-1.13738746737691
66	-1	0.757538989254811	-1.75753898925481
67	-1.7	0.597889115649925	-2.29788911564992
68	-0.7	0.62631868385192	-1.32631868385192
69	-1	0.532278962948479	-1.53227896294848

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.271617070596562	0.543234141193124	0.728382929403438
17	0.138541768660063	0.277083537320127	0.861458231339937
18	0.078927784053455	0.15785556810691	0.921072215946545
19	0.043541970063468	0.087083940126936	0.956458029936532
20	0.0296119228130647	0.0592238456261295	0.970388077186935
21	0.0274416082811100	0.0548832165622201	0.97255839171889
22	0.0146319290352657	0.0292638580705314	0.985368070964734
23	0.00698982869017801	0.0139796573803560	0.993010171309822
24	0.00330579546680039	0.00661159093360077	0.9966942045332
25	0.00154173812244075	0.00308347624488151	0.99845826187756
26	0.000691848191199495	0.00138369638239899	0.9993081518088
27	0.000682503046308733	0.00136500609261747	0.999317496953691
28	0.00033323214137309	0.00066646428274618	0.999666767858627
29	0.000160596463132366	0.000321192926264733	0.999839403536868
30	9.2026202691436e-05	0.000184052405382872	0.999907973797309
31	5.37366783881139e-05	0.000107473356776228	0.999946263321612
32	2.97596600300527e-05	5.95193200601055e-05	0.99997024033997
33	1.81012770883963e-05	3.62025541767927e-05	0.999981898722912
34	1.15872292575225e-05	2.31744585150451e-05	0.999988412770742
35	4.21209320490344e-06	8.42418640980689e-06	0.999995787906795
36	1.50500642268526e-06	3.01001284537052e-06	0.999998494993577
37	6.54242088049186e-07	1.30848417609837e-06	0.999999345757912
38	2.80614837912025e-07	5.6122967582405e-07	0.999999719385162
39	1.29979638438219e-07	2.59959276876439e-07	0.999999870020362
40	7.60870347538039e-08	1.52174069507608e-07	0.999999923912965
41	1.96674797758102e-07	3.93349595516204e-07	0.999999803325202
42	6.42757174953938e-07	1.28551434990788e-06	0.999999357242825
43	2.16452600486395e-06	4.32905200972791e-06	0.999997835473995
44	2.65473640077253e-05	5.30947280154506e-05	0.999973452635992
45	0.000449890900587829	0.000899781801175658	0.999550109099412
46	0.00254254723900535	0.00508509447801069	0.997457452760995
47	0.0050037942149748	0.0100075884299496	0.994996205785025
48	0.0164291107207545	0.0328582214415089	0.983570889279246
49	0.0680893785913216	0.136178757182643	0.931910621408678
50	0.276385333526188	0.552770667052376	0.723614666473812
51	0.36007785048039	0.72015570096078	0.63992214951961
52	0.761874145322949	0.476251709354102	0.238125854677051
53	0.7756739101057	0.448652179788599	0.224326089894299

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	23	0.605263157894737	NOK
5% type I error level	27	0.710526315789474	NOK
10% type I error level	30	0.789473684210526	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/10njb21258717427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/10njb21258717427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/1h9cs1258717427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/1h9cs1258717427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/2fol71258717427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/2fol71258717427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/328m81258717427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/328m81258717427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/4yl8w1258717427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/4yl8w1258717427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/5sepq1258717427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/5sepq1258717427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/6f3vy1258717427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/6f3vy1258717427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/71gn61258717427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/71gn61258717427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/8rjqv1258717427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/8rjqv1258717427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/9tw981258717427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258717478i102gjsx5cbwr5c/9tw981258717427.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')

}

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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