Home » date » 2008 » Dec » 15 »

Investeringsgoederen met seizoenale trend

*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, 15 Dec 2008 00:45:06 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/15/t122932716756q6cughqn0sk8c.htm/, Retrieved Mon, 15 Dec 2008 08:46:07 +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/2008/Dec/15/t122932716756q6cughqn0sk8c.htm/},
    year = {2008},
}
@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 = {2008},
    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 «

97.7 0 101.5 0 119.6 0 108.1 0 117.8 0 125.5 0 89.2 0 92.3 0 104.6 0 122.8 0 96.0 0 94.6 0 93.3 0 101.1 0 114.2 0 104.7 0 113.3 0 118.2 0 83.6 0 73.9 0 99.5 0 97.7 0 103.0 0 106.3 0 92.2 0 101.8 0 122.8 0 111.8 0 106.3 0 121.5 0 81.9 0 85.4 0 110.9 0 117.3 0 106.3 0 105.5 0 101.3 0 105.9 0 126.3 0 111.9 0 108.9 0 127.2 0 94.2 0 85.7 0 116.2 0 107.2 0 110.6 0 112.0 0 104.5 0 112.0 0 132.8 0 110.8 0 128.7 0 136.8 0 94.9 0 88.8 0 123.2 0 125.3 0 122.7 0 125.7 0 116.3 0 118.7 0 142.0 0 127.9 0 131.9 0 152.3 0 110.8 1 99.1 1 135.0 1 133.2 1 131.0 1 133.9 1 119.9 1 136.9 1 148.9 1 145.1 1 142.4 1 159.6 1 120.7 1 109.0 1 142.0 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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 108.853592814371 + 24.8784431137725X[t] -8.80765611633876M1[t] -1.27908468776732M2[t] + 17.1066295979470M3[t] + 4.77805816937554M4[t] + 8.92091531223268M5[t] + 22.0352010265184M6[t] -19.4902908468777M7[t] -25.3617194183062M8[t] + 2.80970915312233M9[t] + 4.25M10[t] -1.40000000000000M11[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)	108.853592814371	3.615278	30.1093	0	0
X	24.8784431137725	2.546303	9.7704	0	0
M1	-8.80765611633876	4.893106	-1.8	0.076294	0.038147
M2	-1.27908468776732	4.893106	-0.2614	0.79457	0.397285
M3	17.1066295979470	4.893106	3.4961	0.000836	0.000418
M4	4.77805816937554	4.893106	0.9765	0.332284	0.166142
M5	8.92091531223268	4.893106	1.8232	0.072675	0.036337
M6	22.0352010265184	4.893106	4.5033	2.7e-05	1.3e-05
M7	-19.4902908468777	4.902112	-3.9759	0.000172	8.6e-05
M8	-25.3617194183062	4.902112	-5.1736	2e-06	1e-06
M9	2.80970915312233	4.902112	0.5732	0.568425	0.284212
M10	4.25	5.077427	0.837	0.405503	0.202752
M11	-1.40000000000000	5.077427	-0.2757	0.783591	0.391796

Multiple Linear Regression - Regression Statistics
Multiple R	0.887443067897231
R-squared	0.78755519875885
Adjusted R-squared	0.750064939716294
F-TEST (value)	21.0069287028634
F-TEST (DF numerator)	12
F-TEST (DF denominator)	68
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	8.79436068323905
Sum Squared Residuals	5259.17302822926

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	97.7	100.045936698033	-2.34593669803259
2	101.5	107.574508126604	-6.07450812660396
3	119.6	125.960222412318	-6.36022241231822
4	108.1	113.631650983747	-5.5316509837468
5	117.8	117.774508126604	0.0254918733960562
6	125.5	130.888793840890	-5.38879384088965
7	89.2	89.3633019674936	-0.16330196749358
8	92.3	83.491873396065	8.80812660393498
9	104.6	111.663301967494	-7.06330196749359
10	122.8	113.103592814371	9.69640718562875
11	96	107.453592814371	-11.4535928143713
12	94.6	108.853592814371	-14.2535928143713
13	93.3	100.045936698032	-6.7459366980325
14	101.1	107.574508126604	-6.47450812660394
15	114.2	125.960222412318	-11.7602224123182
16	104.7	113.631650983747	-8.9316509837468
17	113.3	117.774508126604	-4.47450812660394
18	118.2	130.888793840890	-12.6887938408897
19	83.6	89.3633019674936	-5.76330196749359
20	73.9	83.491873396065	-9.591873396065
21	99.5	111.663301967494	-12.1633019674936
22	97.7	113.103592814371	-15.4035928143713
23	103	107.453592814371	-4.45359281437126
24	106.3	108.853592814371	-2.55359281437126
25	92.2	100.045936698032	-7.84593669803249
26	101.8	107.574508126604	-5.77450812660393
27	122.8	125.960222412318	-3.16022241231823
28	111.8	113.631650983747	-1.83165098374680
29	106.3	117.774508126604	-11.4745081266039
30	121.5	130.888793840890	-9.38879384088965
31	81.9	89.3633019674936	-7.46330196749358
32	85.4	83.491873396065	1.90812660393499
33	110.9	111.663301967494	-0.76330196749358
34	117.3	113.103592814371	4.19640718562874
35	106.3	107.453592814371	-1.15359281437126
36	105.5	108.853592814371	-3.35359281437126
37	101.3	100.045936698032	1.25406330196751
38	105.9	107.574508126604	-1.67450812660393
39	126.3	125.960222412318	0.339777587681775
40	111.9	113.631650983747	-1.73165098374679
41	108.9	117.774508126604	-8.87450812660393
42	127.2	130.888793840890	-3.68879384088965
43	94.2	89.3633019674936	4.83669803250642
44	85.7	83.491873396065	2.20812660393499
45	116.2	111.663301967494	4.53669803250641
46	107.2	113.103592814371	-5.90359281437125
47	110.6	107.453592814371	3.14640718562874
48	112	108.853592814371	3.14640718562874
49	104.5	100.045936698032	4.45406330196751
50	112	107.574508126604	4.42549187339607
51	132.8	125.960222412318	6.83977758768179
52	110.8	113.631650983747	-2.83165098374680
53	128.7	117.774508126604	10.9254918733961
54	136.8	130.888793840890	5.91120615911036
55	94.9	89.3633019674936	5.53669803250642
56	88.8	83.491873396065	5.30812660393499
57	123.2	111.663301967494	11.5366980325064
58	125.3	113.103592814371	12.1964071856287
59	122.7	107.453592814371	15.2464071856288
60	125.7	108.853592814371	16.8464071856287
61	116.3	100.045936698032	16.2540633019675
62	118.7	107.574508126604	11.1254918733961
63	142	125.960222412318	16.0397775876818
64	127.9	113.631650983747	14.2683490162532
65	131.9	117.774508126604	14.1254918733961
66	152.3	130.888793840890	21.4112061591104
67	110.8	114.241745081266	-3.44174508126605
68	99.1	108.370316509837	-9.27031650983747
69	135	136.541745081266	-1.54174508126604
70	133.2	137.982035928144	-4.78203592814372
71	131	132.332035928144	-1.33203592814371
72	133.9	133.732035928144	0.167964071856294
73	119.9	124.924379811805	-5.02437981180494
74	136.9	132.452951240376	4.44704875962362
75	148.9	150.838665526091	-1.93866552609067
76	145.1	138.510094097519	6.58990590248075
77	142.4	142.652951240376	-0.252951240376385
78	159.6	155.767236954662	3.83276304533788
79	120.7	114.241745081266	6.45825491873396
80	109	108.370316509837	0.629683490162526
81	142	136.541745081266	5.45825491873396

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.0591013589218684	0.118202717843737	0.940898641078132
17	0.0292743145737072	0.0585486291474143	0.970725685426293
18	0.0311731995508390	0.0623463991016779	0.968826800449161
19	0.0200546772179028	0.0401093544358056	0.979945322782097
20	0.158149410848667	0.316298821697334	0.841850589151333
21	0.126929901872757	0.253859803745514	0.873070098127243
22	0.515716758557872	0.968566482884257	0.484283241442128
23	0.460063951885569	0.920127903771138	0.539936048114431
24	0.456990433591258	0.913980867182515	0.543009566408743
25	0.407219504059147	0.814439008118294	0.592780495940853
26	0.346513224182917	0.693026448365833	0.653486775817083
27	0.309323552106846	0.618647104213691	0.690676447893154
28	0.263326100031246	0.526652200062492	0.736673899968754
29	0.312087731857362	0.624175463714724	0.687912268142638
30	0.322327513241450	0.644655026482901	0.67767248675855
31	0.318002464033226	0.636004928066453	0.681997535966773
32	0.251084725292942	0.502169450585884	0.748915274707058
33	0.256283138300357	0.512566276600715	0.743716861699643
34	0.223072565974136	0.446145131948272	0.776927434025864
35	0.210144200274358	0.420288400548717	0.789855799725642
36	0.20812813284254	0.41625626568508	0.79187186715746
37	0.190053745762731	0.380107491525461	0.80994625423727
38	0.179966925588556	0.359933851177111	0.820033074411444
39	0.180106677515617	0.360213355031234	0.819893322484383
40	0.168023707631072	0.336047415262144	0.831976292368928
41	0.273086302351761	0.546172604703522	0.726913697648239
42	0.391992954205407	0.783985908410814	0.608007045794593
43	0.384979613324959	0.769959226649919	0.61502038667504
44	0.318531665972705	0.63706333194541	0.681468334027295
45	0.350180960213814	0.700361920427627	0.649819039786186
46	0.430987487420383	0.861974974840767	0.569012512579617
47	0.456373649527414	0.912747299054827	0.543626350472586
48	0.520573428142107	0.958853143715785	0.479426571857893
49	0.514279658829122	0.971440682341757	0.485720341170878
50	0.548368145059981	0.903263709880038	0.451631854940019
51	0.566772639065492	0.866454721869016	0.433227360934508
52	0.840436211181093	0.319127577637813	0.159563788818907
53	0.859225477704364	0.281549044591272	0.140774522295636
54	0.949061064414846	0.101877871170308	0.0509389355851538
55	0.966217118733773	0.0675657625324533	0.0337828812662267
56	0.95414967295407	0.0917006540918602	0.0458503270459301
57	0.956253635076897	0.0874927298462057	0.0437463649231029
58	0.94059240263039	0.118815194739220	0.0594075973696101
59	0.927604722447509	0.144790555104982	0.0723952775524912
60	0.912961125663615	0.174077748672771	0.0870388743363853
61	0.915205190739737	0.169589618520526	0.0847948092602631
62	0.898094776165686	0.203810447668627	0.101905223834314
63	0.854210871451135	0.29157825709773	0.145789128548865
64	0.814998457638663	0.370003084722674	0.185001542361337
65	0.690415597157773	0.619168805684453	0.309584402842226

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	1	0.02	OK
10% type I error level	6	0.12	NOK

Charts produced by software:

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/15/t122932716756q6cughqn0sk8c/182u51229327094.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/15/t122932716756q6cughqn0sk8c/29gd91229327094.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/15/t122932716756q6cughqn0sk8c/46m161229327094.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/15/t122932716756q6cughqn0sk8c/5hmf31229327094.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/15/t122932716756q6cughqn0sk8c/6jhea1229327094.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/15/t122932716756q6cughqn0sk8c/77ybk1229327094.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/15/t122932716756q6cughqn0sk8c/874vh1229327094.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/15/t122932716756q6cughqn0sk8c/9pux11229327094.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/15/t122932716756q6cughqn0sk8c/9pux11229327094.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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