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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: Thu, 19 Nov 2009 02:42:44 -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/19/t1258623845nytbzqxud8xgnhv.htm/, Retrieved Thu, 19 Nov 2009 10:44:18 +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/19/t1258623845nytbzqxud8xgnhv.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 «

8.4 420 8.4 418 8.4 410 8.6 418 8.9 426 8.8 428 8.3 430 7.5 424 7.2 423 7.4 427 8.8 441 9.3 449 9.3 452 8.7 462 8.2 455 8.3 461 8.5 461 8.6 463 8.5 462 8.2 456 8.1 455 7.9 456 8.6 472 8.7 472 8.7 471 8.5 465 8.4 459 8.5 465 8.7 468 8.7 467 8.6 463 8.5 460 8.3 462 8.00 461 8.2 476 8.1 476 8.1 471 8.00 453 7.9 443 7.9 442 8.00 444 8.00 438 7.9 427 8.00 424 7.7 416 7.2 406 7.5 431 7.3 434 7.00 418 7.00 412 7.00 404 7.2 409 7.3 412 7.1 406 6.8 398 6.4 397 6.1 385 6.5 390 7.7 413 7.9 413 7.5 401 6.9 397 6.6 397 6.9 409 7.7 419 8.00 424 8.00 428 7.7 430 7.3 424 7.4 433 8.1 456 8.3 459 8.2 446

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

wgb[t] = -0.654242842014817 + 0.019802240862778nwwz[t] + 0.115514325372899M1[t] -0.0331641461955521M2[t] -0.0711162472541626M3[t] -0.0399296924308296M4[t] + 0.157593930497132M5[t] + 0.187462091072317M6[t] + 0.063535480327318M7[t] -0.180358170561477M8[t] -0.361215126822773M9[t] -0.43761811463981M10[t] -0.0704614379868511M11[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)	-0.654242842014817	1.06298	-0.6155	0.540566	0.270283
nwwz	0.019802240862778	0.002321	8.5308	0	0
M1	0.115514325372899	0.261117	0.4424	0.659801	0.3299
M2	-0.0331641461955521	0.272303	-0.1218	0.903471	0.451736
M3	-0.0711162472541626	0.274768	-0.2588	0.796658	0.398329
M4	-0.0399296924308296	0.272464	-0.1466	0.883978	0.441989
M5	0.157593930497132	0.271233	0.581	0.563397	0.281699
M6	0.187462091072317	0.271398	0.6907	0.492401	0.2462
M7	0.063535480327318	0.272251	0.2334	0.816267	0.408134
M8	-0.180358170561477	0.273216	-0.6601	0.511697	0.255848
M9	-0.361215126822773	0.274991	-1.3136	0.193997	0.096999
M10	-0.43761811463981	0.274407	-1.5948	0.116016	0.058008
M11	-0.0704614379868511	0.269813	-0.2611	0.794872	0.397436

Multiple Linear Regression - Regression Statistics
Multiple R	0.789506840603015
R-squared	0.623321051358955
Adjusted R-squared	0.547985261630746
F-TEST (value)	8.2739034608614
F-TEST (DF numerator)	12
F-TEST (DF denominator)	60
p-value	6.42024722274925e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.467235134244714
Sum Squared Residuals	13.0985202403605

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	8.4	7.77821264572484	0.621787354275162
2	8.4	7.58992969243083	0.810070307569171
3	8.4	7.39355966447	1.00644033553000
4	8.6	7.58316414619555	1.01683585380445
5	8.9	7.93910569602574	0.960894303974263
6	8.8	8.00857833832648	0.791421661673522
7	8.3	7.92425620930704	0.375743790692964
8	7.5	7.56154911324157	-0.0615491132415728
9	7.2	7.3608899161175	-0.160889916117498
10	7.4	7.36369589175157	0.0363041082484261
11	8.8	8.00808394048342	0.791916059516577
12	9.3	8.2369633053725	1.0630366946275
13	9.3	8.41188435333373	0.888115646666269
14	8.7	8.46122829039306	0.238771709606938
15	8.2	8.284660503295	-0.0846605032950062
16	8.3	8.434660503295	-0.134660503295005
17	8.5	8.63218412622297	-0.132184126222968
18	8.6	8.70165676852371	-0.101656768523709
19	8.5	8.55792791691593	-0.0579279169159319
20	8.2	8.19522082085047	0.0047791791495312
21	8.1	7.9945616237264	0.105438376273605
22	7.9	7.93796087677214	-0.0379608767721352
23	8.6	8.62195340722954	-0.0219534072295426
24	8.7	8.6924148452164	0.0075851547836055
25	8.7	8.78812692972651	-0.088126929726515
26	8.5	8.5206350129814	-0.0206350129813956
27	8.4	8.36386946674612	0.0361305332538827
28	8.5	8.51386946674612	-0.0138694667461181
29	8.7	8.77079981226241	-0.0707998122624145
30	8.7	8.78086573197482	-0.0808657319748216
31	8.6	8.57773015777871	0.0222698422212898
32	8.5	8.27442978430158	0.22557021569842
33	8.3	8.13317730976584	0.16682269023416
34	8	8.03697208108603	-0.0369720810860255
35	8.2	8.70116237068065	-0.501162370680655
36	8.1	8.7716238086675	-0.671623808667506
37	8.1	8.78812692972651	-0.688126929726515
38	8	8.28300812262806	-0.283008122628060
39	7.9	8.04703361294167	-0.147033612941669
40	7.9	8.05841792690222	-0.158417926902224
41	8	8.29554603155574	-0.295546031555742
42	8	8.20660074695426	-0.206600746954259
43	7.9	7.8648494867187	0.0351505132812982
44	8	7.56154911324157	0.438450886758428
45	7.7	7.22227423007805	0.477725769921947
46	7.2	6.94784883363324	0.252151166366764
47	7.5	7.81006153185564	-0.310061531855644
48	7.3	7.93992969243083	-0.63992969243083
49	7	7.73860816399928	-0.738608163999281
50	7	7.47111624725416	-0.471116247254162
51	7	7.27474621929333	-0.274746219293328
52	7.2	7.40494397843055	-0.204943978430550
53	7.3	7.66187432394685	-0.361874323946846
54	7.1	7.57292903934536	-0.472929039345364
55	6.8	7.29058450169814	-0.490584501698141
56	6.4	7.02688860994657	-0.626888609946566
57	6.1	6.60840476333194	-0.508404763331936
58	6.5	6.63101297982879	-0.131012979828788
59	7.7	7.45362119632564	0.246378803674360
60	7.9	7.52408263431249	0.375917365687508
61	7.5	7.40197006933206	0.098029930667945
62	6.9	7.17408263431249	-0.274082634312492
63	6.6	7.13613053325388	-0.536130533253882
64	6.9	7.40494397843055	-0.50494397843055
65	7.7	7.8004900099863	-0.100490009986292
66	8	7.92936937487537	0.0706306251246328
67	8	7.88465172758148	0.11534827241852
68	7.7	7.68036255841824	0.0196374415817599
69	7.3	7.38069215698028	-0.0806921569802773
70	7.4	7.48250933692824	-0.0825093369282414
71	8.1	8.3051175534251	-0.205117553425095
72	8.3	8.43498571400028	-0.134985714000279
73	8.2	8.29307090815706	-0.0930709081570652

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.863465141652694	0.273069716694611	0.136534858347306
17	0.841057600577712	0.317884798844575	0.158942399422288
18	0.759605710688824	0.480788578622352	0.240394289311176
19	0.665317603525141	0.669364792949717	0.334682396474859
20	0.734250763929938	0.531498472140124	0.265749236070062
21	0.82457734757269	0.350845304854619	0.175422652427310
22	0.781599832946927	0.436800334106146	0.218400167053073
23	0.734975623879527	0.530048752240946	0.265024376120473
24	0.777029695819125	0.445940608361749	0.222970304180875
25	0.726410448602794	0.547179102794413	0.273589551397206
26	0.65791087516154	0.684178249676921	0.342089124838460
27	0.586093342171736	0.827813315656527	0.413906657828264
28	0.506075992810186	0.987848014379628	0.493924007189814
29	0.422544948606026	0.845089897212053	0.577455051393974
30	0.340637464549535	0.68127492909907	0.659362535450465
31	0.274193675809119	0.548387351618238	0.725806324190881
32	0.320985356846449	0.641970713692899	0.679014643153551
33	0.345762718600344	0.691525437200687	0.654237281399656
34	0.289387089364079	0.578774178728158	0.710612910635921
35	0.331986111833099	0.663972223666197	0.668013888166901
36	0.54322988129537	0.91354023740926	0.45677011870463
37	0.668786322883308	0.662427354233384	0.331213677116692
38	0.648998125487406	0.702003749025189	0.351001874512595
39	0.60667937767992	0.78664124464016	0.39332062232008
40	0.581782999628601	0.836434000742799	0.418217000371399
41	0.583007827035757	0.833984345928486	0.416992172964243
42	0.568272295237384	0.863455409525233	0.431727704762617
43	0.523585793711755	0.95282841257649	0.476414206288245
44	0.5593329155924	0.881334168815199	0.440667084407599
45	0.624894914562858	0.750210170874285	0.375105085437142
46	0.597946368588682	0.804107262822636	0.402053631411318
47	0.627906160493576	0.744187679012847	0.372093839506424
48	0.822945360806356	0.354109278387288	0.177054639193644
49	0.940780476296832	0.118439047406336	0.059219523703168
50	0.931340377711374	0.137319244577252	0.068659622288626
51	0.908752439860178	0.182495120279643	0.0912475601398217
52	0.876533102619551	0.246933794760898	0.123466897380449
53	0.829000285735263	0.341999428529474	0.170999714264737
54	0.81955288081474	0.360894238370518	0.180447119185259
55	0.831953526911647	0.336092946176706	0.168046473088353
56	0.897965164806293	0.204069670387415	0.102034835193707
57	0.949248262969845	0.101503474060309	0.0507517370301546

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	0	0	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/10uspz1258623759.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/10uspz1258623759.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/10uz11258623759.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/10uz11258623759.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/28vr41258623759.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/28vr41258623759.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/3i8pc1258623759.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/3i8pc1258623759.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/4idft1258623759.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/4idft1258623759.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/5u6cw1258623759.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/5u6cw1258623759.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/6aq8p1258623759.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/6aq8p1258623759.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/7hks61258623759.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/7hks61258623759.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/8z6jm1258623759.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/8z6jm1258623759.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/9rqum1258623759.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258623845nytbzqxud8xgnhv/9rqum1258623759.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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