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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: Mon, 23 Nov 2009 06:50:07 -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/23/t1258985247s5iwrm004vn4pto.htm/, Retrieved Mon, 23 Nov 2009 15:07:42 +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/23/t1258985247s5iwrm004vn4pto.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 «

7.0 519 6.9 517 6.7 510 6.7 509 6.5 501 6.4 507 6.5 569 6.5 580 6.5 578 6.7 565 6.8 547 7.2 555 7.6 562 7.6 561 7.2 555 6.4 544 6.1 537 6.3 543 7.1 594 7.5 611 7.4 613 7.1 611 6.8 594 6.9 595 7.2 591 7.4 589 7.3 584 6.9 573 6.9 567 6.8 569 7.1 621 7.2 629 7.1 628 7.0 612 6.9 595 7.1 597 7.3 593 7.5 590 7.5 580 7.5 574 7.3 573 7.0 573 6.7 620 6.5 626 6.5 620 6.5 588 6.6 566 6.8 557 6.9 561 6.9 549 6.8 532 6.8 526 6.5 511 6.1 499 6.1 555 5.9 565 5.7 542 5.9 527 5.9 510 6.1 514 6.3 517 6.2 508 5.9 493 5.7 490 5.4 469 5.6 478 6.2 528 6.3 534 6.0 518 5.6 506 5.5 502 5.9 516

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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

wkgo[t] = -0.104584032567092 + 0.0121858140958016werkl[t] + 0.365054612189629M1[t] + 0.457286046986006M2[t] + 0.395810854610688M3[t] + 0.239654343884098M4[t] + 0.140783880143514M5[t] + 0.035109887634544M6[t] -0.360738259442941M7[t] -0.44520112903569M8[t] -0.468443220967877M9[t] -0.352322676197521M10[t] -0.209380619680662M11[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)	-0.104584032567092	0.59295	-0.1764	0.8606	0.4303
werkl	0.0121858140958016	0.001042	11.69	0	0
M1	0.365054612189629	0.179337	2.0356	0.046292	0.023146
M2	0.457286046986006	0.179364	2.5495	0.013407	0.006704
M3	0.395810854610688	0.179868	2.2006	0.031694	0.015847
M4	0.239654343884098	0.180499	1.3277	0.18938	0.09469
M5	0.140783880143514	0.181919	0.7739	0.44209	0.221045
M6	0.035109887634544	0.181607	0.1933	0.847366	0.423683
M7	-0.360738259442941	0.18129	-1.9898	0.051249	0.025625
M8	-0.44520112903569	0.183039	-2.4323	0.018057	0.009028
M9	-0.468443220967877	0.181607	-2.5794	0.012408	0.006204
M10	-0.352322676197521	0.179803	-1.9595	0.054785	0.027393
M11	-0.209380619680662	0.179364	-1.1673	0.247766	0.123883

Multiple Linear Regression - Regression Statistics
Multiple R	0.865445239888877
R-squared	0.748995463246315
Adjusted R-squared	0.697943693059125
F-TEST (value)	14.6712926995478
F-TEST (DF numerator)	12
F-TEST (DF denominator)	59
p-value	1.46327394645596e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.310609709478428
Sum Squared Residuals	5.69222510571412

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	7	6.58490809534358	0.415091904656419
2	6.9	6.65276790194834	0.247232098051658
3	6.7	6.50599201090241	0.194007989097586
4	6.7	6.33764968608002	0.362350313919977
5	6.5	6.14129270957303	0.358707290426975
6	6.4	6.10873360163887	0.291266398361135
7	6.5	6.46840592850108	0.0315940714989197
8	6.5	6.51798701396215	-0.0179870139621493
9	6.5	6.47037329383836	0.0296267061616418
10	6.7	6.4280782553633	0.271921744636705
11	6.8	6.35167565815573	0.448324341844274
12	7.2	6.6585427906028	0.541457209397201
13	7.6	7.10889810146304	0.491101898536961
14	7.6	7.18894372216361	0.411056277836385
15	7.2	7.05435364521349	0.145646354786513
16	6.4	6.76415317943308	-0.364153179433079
17	6.1	6.57998201702188	-0.479982017021884
18	6.3	6.54742290908772	-0.247422909087724
19	7.1	6.77305128089612	0.326948719103879
20	7.5	6.895747250932	0.604252749068001
21	7.4	6.89687678719141	0.503123212808586
22	7.1	6.98862570377017	0.111374296229831
23	6.8	6.9244089206584	-0.124408920658400
24	6.9	7.14597535443486	-0.245975354434863
25	7.2	7.46228671024129	-0.262286710241286
26	7.4	7.53014651684606	-0.130146516846059
27	7.3	7.40774225399173	-0.107742253991734
28	6.9	7.11754178821133	-0.217541788211326
29	6.9	6.94555643989593	-0.0455564398959318
30	6.8	6.86425407557857	-0.0642540755785657
31	7.1	7.10206826148276	-0.00206826148276437
32	7.2	7.11509190465643	0.0849080953435722
33	7.1	7.07966399862844	0.0203360013715608
34	7	7.00081151786597	-0.000811517865970363
35	6.9	6.9365947347542	-0.0365947347542015
36	7.1	7.17034698262647	-0.0703469826264671
37	7.3	7.48665833843289	-0.186658338432889
38	7.5	7.54233233094186	-0.0423323309418608
39	7.5	7.35899899760853	0.141001002391473
40	7.5	7.12972760230713	0.370272397692872
41	7.3	7.01867132447074	0.281328675529258
42	7	6.91299733196177	0.087002668038228
43	6.7	7.08988244738696	-0.389882447386962
44	6.5	7.07853446236902	-0.578534462369023
45	6.5	6.98217748586203	-0.482177485862026
46	6.5	6.70835197956673	-0.208351979566732
47	6.6	6.58320612597596	0.0167938740240445
48	6.8	6.6829144187944	0.117085581205598
49	6.9	7.09671228736724	-0.196712287367237
50	6.9	7.042713953014	-0.142713953013995
51	6.8	6.77407992101005	0.0259200789899497
52	6.8	6.54480852570865	0.255191474291349
53	6.5	6.26315085053104	0.236849149468958
54	6.1	6.01124708887245	0.0887529111275468
55	6.1	6.29780453115986	-0.197804531159858
56	5.9	6.33519980252512	-0.435199802525125
57	5.7	6.0316839863895	-0.3316839863895
58	5.9	5.96501731972283	-0.0650173197228333
59	5.9	5.90080053661107	-0.000800536611064745
60	6.1	6.15892441267493	-0.0589244126749334
61	6.3	6.56053646715197	-0.260536467151967
62	6.2	6.54309557508613	-0.343095575086129
63	5.9	6.29883317127379	-0.398833171273787
64	5.7	6.10611921825979	-0.406119218259793
65	5.4	5.75134665850737	-0.351346658507374
66	5.6	5.75534499286062	-0.155344992860619
67	6.2	5.96878755057321	0.231212449426786
68	6.3	5.95743956555528	0.342560434444725
69	6	5.73922444809026	0.260775551909738
70	5.6	5.709115223711	-0.109115223711
71	5.5	5.80331402384465	-0.303314023844652
72	5.9	6.18329604086654	-0.283296040866536

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.64260424703547	0.714791505929061	0.357395752964531
17	0.821357448444205	0.357285103111591	0.178642551555795
18	0.769377814994865	0.461244370010271	0.230622185005135
19	0.780610448276292	0.438779103447416	0.219389551723708
20	0.928138054392847	0.143723891214306	0.071861945607153
21	0.961643415309013	0.0767131693819743	0.0383565846909871
22	0.938475837443685	0.12304832511263	0.0615241625563149
23	0.930127891592491	0.139744216815018	0.0698721084075088
24	0.944410271750271	0.111179456499458	0.055589728249729
25	0.939789219098257	0.120421561803487	0.0602107809017435
26	0.910005839183051	0.179988321633897	0.0899941608169487
27	0.868751237668484	0.262497524663033	0.131248762331516
28	0.837850577301215	0.324298845397571	0.162149422698785
29	0.808609284139804	0.382781431720393	0.191390715860196
30	0.755040011777652	0.489919976444697	0.244959988222348
31	0.683869214912704	0.632261570174591	0.316130785087296
32	0.627213684632036	0.745572630735929	0.372786315367964
33	0.554688234150628	0.890623531698743	0.445311765849372
34	0.47389682743637	0.94779365487274	0.52610317256363
35	0.391973723668541	0.783947447337083	0.608026276331459
36	0.31636781351264	0.63273562702528	0.68363218648736
37	0.257935093316135	0.515870186632271	0.742064906683865
38	0.201422672381237	0.402845344762475	0.798577327618763
39	0.182977828412421	0.365955656824842	0.81702217158758
40	0.287274276731751	0.574548553463502	0.712725723268249
41	0.350315795793165	0.70063159158633	0.649684204206835
42	0.311107097867412	0.622214195734824	0.688892902132588
43	0.302677990024756	0.605355980049512	0.697322009975244
44	0.434169378309363	0.868338756618726	0.565830621690637
45	0.550097286357976	0.899805427284048	0.449902713642024
46	0.533620006062952	0.932759987874097	0.466379993937048
47	0.442952149898904	0.885904299797808	0.557047850101096
48	0.359934088842864	0.719868177685727	0.640065911157136
49	0.292402347907277	0.584804695814553	0.707597652092723
50	0.228457466688798	0.456914933377595	0.771542533311202
51	0.187357097737404	0.374714195474808	0.812642902262596
52	0.251356828660325	0.502713657320651	0.748643171339675
53	0.580584228347307	0.838831543305386	0.419415771652693
54	0.705714077378455	0.58857184524309	0.294285922621545
55	0.576307561596904	0.847384876806192	0.423692438403096
56	0.553168255526429	0.893663488947142	0.446831744473571

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	0.024390243902439	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/10o6s11258984202.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/10o6s11258984202.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/1zv7v1258984201.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/1zv7v1258984201.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/25ki31258984201.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/25ki31258984201.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/349xw1258984201.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/349xw1258984201.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/4fn3w1258984201.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/4fn3w1258984201.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/5zwqf1258984201.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/5zwqf1258984201.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/6fhbj1258984201.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/6fhbj1258984201.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/7bi2m1258984201.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/7bi2m1258984201.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/8c8c61258984201.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/8c8c61258984201.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/9lt9g1258984202.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258985247s5iwrm004vn4pto/9lt9g1258984202.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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