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multiple regression + seasonal dummy

*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: Sat, 18 Dec 2010 15:38:04 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr.htm/, Retrieved Sat, 18 Dec 2010 16:37:06 +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/2010/Dec/18/t1292686616xiuqokhjmtl06cr.htm/},
    year = {2010},
}
@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 = {2010},
    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 «

14544.5 94.6 -3.0 14097.8 15116.3 95.9 -3.7 14776.8 17413.2 104.7 -4.7 16833.3 16181.5 102.8 -6.4 15385.5 15607.4 98.1 -7.5 15172.6 17160.9 113.9 -7.8 16858.9 14915.8 80.9 -7.7 14143.5 13768 95.7 -6.6 14731.8 17487.5 113.2 -4.2 16471.6 16198.1 105.9 -2.0 15214 17535.2 108.8 -0.7 17637.4 16571.8 102.3 0.1 17972.4 16198.9 99 0.9 16896.2 16554.2 100.7 2.1 16698 19554.2 115.5 3.5 19691.6 15903.8 100.7 4.9 15930.7 18003.8 109.9 5.7 17444.6 18329.6 114.6 6.2 17699.4 16260.7 85.4 6.5 15189.8 14851.9 100.5 6.5 15672.7 18174.1 114.8 6.3 17180.8 18406.6 116.5 6.2 17664.9 18466.5 112.9 6.4 17862.9 16016.5 102 6.3 16162.3 17428.5 106 5.8 17463.6 17167.2 105.3 5.1 16772.1 19630 118.8 5.1 19106.9 17183.6 106.1 5.8 16721.3 18344.7 109.3 6.7 18161.3 19301.4 117.2 7.1 18509.9 18147.5 92.5 6.7 17802.7 16192.9 104.2 5.5 16409.9 18374.4 112.5 4.2 17967.7 20515.2 122.4 3.0 20286.6 18957.2 113.3 2.2 19537.3 16471.5 100 2.0 18021.9 18746.8 110.7 1.8 20194.3 19009.5 112.8 1.8 19049.6 19211.2 109 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	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

productie[t] = + 46.1552447002207 + 0.00505160940148495uitvoer[t] + 0.0383667038748176ondernemersvertrouwen[t] -0.00168342883758715invoer[t] -0.811036404092018M1[t] -0.973083226425028M2[t] + 2.90543493194508M3[t] + 0.833635686943556M4[t] -0.761320508547857M5[t] + 3.8949428776532M6[t] -16.7307285401992M7[t] + 2.80207449536563M8[t] + 4.80789720302752M9[t] + 5.7307324544371M10[t] + 2.13148046506502M11[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)	46.1552447002207	6.365331	7.251	0	0
uitvoer	0.00505160940148495	0.001218	4.1488	0.000122	6.1e-05
ondernemersvertrouwen	0.0383667038748176	0.064588	0.594	0.555026	0.277513
invoer	-0.00168342883758715	0.000934	-1.8021	0.077217	0.038609
M1	-0.811036404092018	2.031718	-0.3992	0.691359	0.34568
M2	-0.973083226425028	2.304074	-0.4223	0.674491	0.337246
M3	2.90543493194508	2.665866	1.0899	0.280703	0.140351
M4	0.833635686943556	2.318668	0.3595	0.720626	0.360313
M5	-0.761320508547857	2.424216	-0.314	0.754717	0.377359
M6	3.8949428776532	2.884573	1.3503	0.182668	0.091334
M7	-16.7307285401992	2.654719	-6.3023	0	0
M8	2.80207449536563	2.008727	1.395	0.16885	0.084425
M9	4.80789720302752	2.835534	1.6956	0.095832	0.047916
M10	5.7307324544371	2.741573	2.0903	0.041403	0.020701
M11	2.13148046506502	2.368087	0.9001	0.372146	0.186073

Multiple Linear Regression - Regression Statistics
Multiple R	0.956648353467096
R-squared	0.915176072191306
Adjusted R-squared	0.892769751638066
F-TEST (value)	40.8445496446749
F-TEST (DF numerator)	14
F-TEST (DF denominator)	53
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.25824317233053
Sum Squared Residuals	562.65587421204

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	94.6	94.969598057866	-0.369598057866019
2	95.9	96.526156617868	-0.626156617868087
3	104.7	108.507378302136	-3.80737830213619
4	102.8	102.585556631797	0.214443368202853
5	98.1	98.4066701041732	-0.306670104173226
6	113.9	108.060332635596	5.83966736440451
7	80.9	80.6683122864409	0.231687713559128
8	95.7	93.454720240091	2.24527975990896
9	113.2	111.413254714242	1.78674528575835
10	105.9	108.024031658051	-2.12403165805073
11	108.8	107.149541869433	1.65045813056723
12	102.3	99.6180856094853	2.68191439051471
13	99	98.7657035376907	0.234296462309329
14	100.7	100.778189175965	-0.0781891759648243
15	115.5	114.825736356014	0.67426364398636
16	100.7	100.698463052538	0.00153694746230977
17	109.9	107.194037046041	2.70596295395865
18	114.6	113.086360459366	1.51363954063359
19	85.4	86.245657372753	-0.845657372752984
20	100.5	97.848825297835	2.65117470216506
21	114.8	114.09065238837	0.709347611630004
22	116.5	115.369202254961	1.13079774503859
23	112.9	111.746896099671	1.15310390032901
24	102	100.097975011781	1.90202498821894
25	106	104.209981784296	1.79001821570377
26	105.3	103.865183773834	1.43481622616565
27	118.8	116.254335916183	2.54566408381689
28	106.1	105.867123959049	0.232876040950923
29	109.3	107.747983946984	1.55201605301631
30	117.2	116.665625436352	0.534374563647559
31	92.5	91.3860761225183	1.11392387748171
32	104.2	103.343643062282	0.856356937717804
33	112.5	113.697229521053	-1.197229521053
34	122.4	121.484807003031	0.915192996969053
35	113.3	111.245847431050	2.05415256895049
36	100	99.100976196418	0.899023803582064
37	110.7	106.119112515975	4.58088748402466
38	112.8	109.211144473798	3.58885552620155
39	109.8	112.085196433485	-2.28519643348522
40	117.3	114.654409109446	2.64559089055353
41	109.1	109.866681550249	-0.76668155024892
42	115.9	118.634337700095	-2.73433770009517
43	96	96.7073036832525	-0.707303683252473
44	99.8	99.684088503765	0.115911496235029
45	116.8	117.655097115629	-0.855097115629007
46	115.7	116.512477458939	-0.812477458939358
47	99.4	97.5476323575325	1.85236764246753
48	94.3	91.17694524485	3.12305475515003
49	91	88.844677011853	2.15532298814703
50	93.2	91.8351713002317	1.36482869976828
51	103.1	98.7399774263942	4.36002257360574
52	94.1	94.5684811180168	-0.468481118016770
53	91.8	90.5961728917603	1.20382710823968
54	102.7	102.319358498603	0.380641501397293
55	82.6	79.0446914694287	3.55530853057128
56	89.1	88.6521973895424	0.447802610457574
57	104.5	104.943766260706	-0.443766260706347
58	105.1	104.209481625018	0.89051837498245
59	95.1	101.810082242314	-6.71008224231426
60	88.7	97.3060179374658	-8.60601793746575
61	86.3	94.6909270923188	-8.39092709231877
62	91.8	97.4841546583026	-5.68415465830257
63	111.5	112.987375565788	-1.48737556578758
64	99.7	102.325966129153	-2.62596612915285
65	97.5	101.888454460793	-4.3884544607925
66	111.7	117.233985269988	-5.53398526998778
67	86.2	89.5479590656066	-3.34795906560665
68	95.4	101.716525506484	-6.31652550648442

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
18	0.204241237356359	0.408482474712718	0.795758762643641
19	0.0915902683032055	0.183180536606411	0.908409731696795
20	0.0448605062004119	0.0897210124008238	0.955139493799588
21	0.0177213931126614	0.0354427862253227	0.982278606887339
22	0.00718135558994334	0.0143627111798867	0.992818644410057
23	0.00248608533915074	0.00497217067830148	0.99751391466085
24	0.00146120360638945	0.00292240721277891	0.99853879639361
25	0.000507176965821918	0.00101435393164384	0.999492823034178
26	0.000188818391732301	0.000377636783464601	0.999811181608268
27	0.000359906954673567	0.000719813909347134	0.999640093045326
28	0.000139996488128244	0.000279992976256488	0.999860003511872
29	6.60396745814442e-05	0.000132079349162888	0.999933960325418
30	0.000346044834059082	0.000692089668118165	0.99965395516594
31	0.000178433378315343	0.000356866756630687	0.999821566621685
32	0.000236641577457648	0.000473283154915297	0.999763358422542
33	0.000323891164706825	0.00064778232941365	0.999676108835293
34	0.000253753017868478	0.000507506035736956	0.999746246982132
35	0.000270165696905012	0.000540331393810023	0.999729834303095
36	0.000457255613132525	0.000914511226265049	0.999542744386867
37	0.00221800157764916	0.00443600315529833	0.99778199842235
38	0.0481614440705238	0.0963228881410476	0.951838555929476
39	0.0506935151423605	0.101387030284721	0.94930648485764
40	0.187673679871330	0.375347359742659	0.81232632012867
41	0.499255375715733	0.998510751431466	0.500744624284267
42	0.706032052838019	0.587935894323963	0.293967947161981
43	0.63947114622992	0.721057707540159	0.360528853770080
44	0.553613514651145	0.892772970697711	0.446386485348855
45	0.459031595357453	0.918063190714906	0.540968404642547
46	0.385188685358491	0.770377370716982	0.614811314641509
47	0.319090938472801	0.638181876945601	0.6809090615272
48	0.478876520128577	0.957753040257155	0.521123479871423
49	0.637374651467775	0.725250697064451	0.362625348532225
50	0.80209534102294	0.395809317954119	0.197904658977059

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	15	0.454545454545455	NOK
5% type I error level	17	0.515151515151515	NOK
10% type I error level	19	0.575757575757576	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/10k3d11292686676.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/10k3d11292686676.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/16bga1292686676.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/16bga1292686676.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/26bga1292686676.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/26bga1292686676.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/36bga1292686676.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/36bga1292686676.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/4h2fv1292686676.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/4h2fv1292686676.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/5h2fv1292686676.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/5h2fv1292686676.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/6h2fv1292686676.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/6h2fv1292686676.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/79bwf1292686676.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/79bwf1292686676.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/89bwf1292686676.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/89bwf1292686676.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/9k3d11292686676.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292686616xiuqokhjmtl06cr/9k3d11292686676.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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