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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, 10 Dec 2010 11:36:32 +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/10/t12919809108n2jz5jlz8cju0r.htm/, Retrieved Fri, 10 Dec 2010 12:35:21 +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/10/t12919809108n2jz5jlz8cju0r.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 «

9700 0 9081 0 9084 0 9743 0 8587 0 9731 0 9563 0 9998 0 9437 0 10038 0 9918 0 9252 0 9737 0 9035 0 9133 0 9487 0 8700 0 9627 0 8947 0 9283 0 8829 0 9947 0 9628 0 9318 0 9605 0 8640 0 9214 0 9567 0 8547 0 9185 0 9470 0 9123 0 9278 0 10170 0 9434 0 9655 0 9429 0 8739 0 9552 0 9687 0 9019 1 9672 1 9206 1 9069 1 9788 1 10312 1 10105 1 9863 1 9656 1 9295 1 9946 1 9701 1 9049 1 10190 1 9706 1 9765 1 9893 1 9994 1 10433 1 10073 1 10112 1 9266 1 9820 1 10097 1 9115 1 10411 1 9678 1 10408 1 10153 1 10368 1 10581 1 10597 1 10680 1 9738 1 9556 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	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Birth[t] = + 9433.57874165147 + 302.401411657555x[t] + 98.9596904183565M1[t] -638.140798927342M2[t] -284.384145415907M3[t] + 10.727959565733M4[t] -922.129907913377M5[t] + 39.4124598837721M6[t] -339.878505652412M7[t] -165.50280452193M8[t] -215.127103391447M9[t] + 355.081931072368M10[t] + 228.457632202851M11[t] + 4.95763220285092t + 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)	9433.57874165147	139.583711	67.5837	0	0
x	302.401411657555	132.823529	2.2767	0.026324	0.013162
M1	98.9596904183565	157.795511	0.6271	0.532909	0.266454
M2	-638.140798927342	157.688002	-4.0469	0.000149	7.4e-05
M3	-284.384145415907	157.639891	-1.804	0.076167	0.038084
M4	10.727959565733	163.967849	0.0654	0.948048	0.474024
M5	-922.129907913377	164.913195	-5.5916	1e-06	0
M6	39.4124598837721	164.543343	0.2395	0.811501	0.40575
M7	-339.878505652412	164.229741	-2.0695	0.042739	0.02137
M8	-165.50280452193	163.972711	-1.0093	0.316803	0.158401
M9	-215.127103391447	163.77252	-1.3136	0.193909	0.096955
M10	355.081931072368	163.629376	2.17	0.033908	0.016954
M11	228.457632202851	163.54343	1.3969	0.167498	0.083749
t	4.95763220285092	3.06155	1.6193	0.110537	0.055269

Multiple Linear Regression - Regression Statistics
Multiple R	0.85949714743181
R-squared	0.73873534644342
Adjusted R-squared	0.683055994046116
F-TEST (value)	13.2676713114786
F-TEST (DF numerator)	13
F-TEST (DF denominator)	61
p-value	2.94209101525666e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	283.215891843633
Sum Squared Residuals	4892885.72495986

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	9700	9537.49606427275	162.503935727248
2	9081	8805.35320712984	275.646792870157
3	9084	9164.06749284413	-80.0674928441278
4	9743	9464.13723002862	278.862769971378
5	8587	8536.23699475236	50.7630052476382
6	9731	9502.73699475236	228.263005247639
7	9563	9128.40366141903	434.596338580973
8	9998	9307.73699475236	690.263005247639
9	9437	9263.0703280857	173.929671914305
10	10038	9838.23699475236	199.763005247639
11	9918	9716.5703280857	201.429671914305
12	9252	9493.0703280857	-241.070328085695
13	9737	9596.9876507069	140.012349293097
14	9035	8864.84479356405	170.155206435946
15	9133	9223.55907927834	-90.55907927834
16	9487	9523.62881646283	-36.6288164628315
17	8700	8595.72858118657	104.271418813428
18	9627	9562.22858118657	64.7714188134276
19	8947	9187.89524785324	-240.895247853239
20	9283	9367.22858118657	-84.2285811865722
21	8829	9322.5619145199	-493.561914519906
22	9947	9897.72858118657	49.2714188134276
23	9628	9776.0619145199	-148.061914519906
24	9318	9552.5619145199	-234.561914519906
25	9605	9656.47923714111	-51.4792371411137
26	8640	8924.33637999827	-284.336379998265
27	9214	9283.05066571255	-69.0506657125511
28	9567	9583.12040289704	-16.1204028970426
29	8547	8655.22016762078	-108.220167620784
30	9185	9621.72016762078	-436.720167620783
31	9470	9247.38683428745	222.613165712550
32	9123	9426.72016762078	-303.720167620783
33	9278	9382.05350095412	-104.053500954117
34	10170	9957.22016762078	212.779832379217
35	9434	9835.55350095412	-401.553500954117
36	9655	9612.05350095412	42.946499045883
37	9429	9715.97082357532	-286.970823575325
38	8739	8983.82796643248	-244.827966432476
39	9552	9342.54225214676	209.457747853238
40	9687	9642.61198933125	44.3880106687463
41	9019	9017.11316571255	1.88683428745027
42	9672	9983.61316571255	-311.61316571255
43	9206	9609.27983237922	-403.279832379217
44	9069	9788.61316571255	-719.613165712549
45	9788	9743.94649904588	44.0535009541169
46	10312	10319.1131657125	-7.11316571254994
47	10105	10197.4464990459	-92.4464990458831
48	9863	9973.94649904588	-110.946499045883
49	9656	10077.8638216671	-421.863821667091
50	9295	9345.72096452424	-50.7209645242425
51	9946	9704.43525023853	241.564749761472
52	9701	10004.5049874230	-303.50498742302
53	9049	9076.60475214676	-27.6047521467608
54	10190	10043.1047521468	146.895247853239
55	9706	9668.77141881343	37.2285811865722
56	9765	9848.10475214676	-83.1047521467606
57	9893	9803.4380854801	89.5619145199058
58	9994	10378.6047521468	-384.604752146761
59	10433	10256.9380854801	176.061914519906
60	10073	10033.4380854801	39.5619145199056
61	10112	10137.3554081013	-25.3554081013022
62	9266	9405.21255095845	-139.212550958454
63	9820	9763.92683667274	56.0731633272605
64	10097	10063.9965738572	33.003426142769
65	9115	9136.09633858097	-21.0963385809719
66	10411	10102.5963385810	308.403661419028
67	9678	9728.26300524764	-50.2630052476388
68	10408	9907.59633858097	500.403661419028
69	10153	9862.9296719143	290.070328085695
70	10368	10438.0963385810	-70.0963385809721
71	10581	10316.4296719143	264.570328085695
72	10597	10092.9296719143	504.070328085695
73	10680	10196.8469945355	483.153005464487
74	9738	9464.70413739267	273.295862607335
75	9556	9823.41842310695	-267.418423106951

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.128558701500382	0.257117403000764	0.871441298499618
18	0.0584272284332547	0.116854456866509	0.941572771566745
19	0.331902517617099	0.663805035234199	0.6680974823829
20	0.550217150357053	0.899565699285895	0.449782849642947
21	0.586066000730772	0.827867998538456	0.413933999269228
22	0.512632885593036	0.974734228813928	0.487367114406964
23	0.407042968497683	0.814085936995367	0.592957031502317
24	0.369777912385374	0.739555824770749	0.630222087614626
25	0.318178362712117	0.636356725424234	0.681821637287883
26	0.248733708478292	0.497467416956583	0.751266291521708
27	0.279598148959271	0.559196297918542	0.720401851040729
28	0.23937542318131	0.47875084636262	0.76062457681869
29	0.179823048953785	0.35964609790757	0.820176951046215
30	0.197507730420186	0.395015460840371	0.802492269579814
31	0.294278771657167	0.588557543314335	0.705721228342833
32	0.28327434334654	0.56654868669308	0.71672565665346
33	0.286476358634648	0.572952717269295	0.713523641365352
34	0.365848282928086	0.731696565856171	0.634151717071914
35	0.370001253067677	0.740002506135353	0.629998746932323
36	0.438541623561671	0.877083247123343	0.561458376438329
37	0.390370261349309	0.780740522698618	0.609629738650691
38	0.366315396708289	0.732630793416579	0.633684603291711
39	0.457163000114408	0.914326000228816	0.542836999885592
40	0.396643297800992	0.793286595601984	0.603356702199008
41	0.362487553133524	0.724975106267049	0.637512446866476
42	0.325459407342082	0.650918814684165	0.674540592657918
43	0.299090327214512	0.598180654429025	0.700909672785488
44	0.561661054754946	0.876677890490108	0.438338945245054
45	0.577974413688325	0.844051172623349	0.422025586311675
46	0.640562753076958	0.718874493846085	0.359437246923042
47	0.575903352210572	0.848193295578857	0.424096647789428
48	0.512204152551337	0.975591694897325	0.487795847448663
49	0.569473229415518	0.861053541168965	0.430526770584482
50	0.49126777097515	0.9825355419503	0.50873222902485
51	0.784439926672479	0.431120146655042	0.215560073327521
52	0.711834598962827	0.576330802074346	0.288165401037173
53	0.654075034906263	0.691849930187475	0.345924965093737
54	0.588325914380174	0.823348171239652	0.411674085619826
55	0.582928354827184	0.834143290345632	0.417071645172816
56	0.560199089325349	0.879601821349301	0.439800910674651
57	0.429938510892681	0.859877021785362	0.570061489107319
58	0.294644039324648	0.589288078649297	0.705355960675352

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/2010/Dec/10/t12919809108n2jz5jlz8cju0r/10bkyp1291980983.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/10bkyp1291980983.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/14j1e1291980983.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/14j1e1291980983.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/24j1e1291980983.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/24j1e1291980983.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/3xs0h1291980983.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/3xs0h1291980983.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/4xs0h1291980983.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/4xs0h1291980983.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/5xs0h1291980983.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/5xs0h1291980983.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/6p2zk1291980983.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/6p2zk1291980983.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/70ty41291980983.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/70ty41291980983.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/80ty41291980983.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/80ty41291980983.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/90ty41291980983.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919809108n2jz5jlz8cju0r/90ty41291980983.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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