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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, 20 Nov 2009 11:57:11 -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/20/t1258743452k9pm3n9hm4s2rqa.htm/, Retrieved Fri, 20 Nov 2009 19:57:45 +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/20/t1258743452k9pm3n9hm4s2rqa.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 «

519164 0,9 517009 1,3 509933 1,4 509127 1,5 500857 1,1 506971 1,6 569323 1,5 579714 1,6 577992 1,7 565464 1,6 547344 1,7 554788 1,6 562325 1,6 560854 1,3 555332 1,1 543599 1,6 536662 1,9 542722 1,6 593530 1,7 610763 1,6 612613 1,4 611324 2,1 594167 1,9 595454 1,7 590865 1,8 589379 2 584428 2,5 573100 2,1 567456 2,1 569028 2,3 620735 2,4 628884 2,4 628232 2,3 612117 1,7 595404 2 597141 2,3 593408 2 590072 2 579799 1,3 574205 1,7 572775 1,9 572942 1,7 619567 1,6 625809 1,7 619916 1,8 587625 1,9 565742 1,9 557274 1,9 560576 2 548854 2,1 531673 1,9 525919 1,9 511038 1,3 498662 1,3 555362 1,4 564591 1,2 541657 1,3 527070 1,8 509846 2,2 514258 2,6 516922 2,8 507561 3,1 492622 3,9 490243 3,7 469357 4,6 477580 5,1 528379 5,2 533590 4,9 517945 5,1 506174 4,8 501866 3,9 516141 3,5

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

TWIB[t] = + 594927.970623835 -17243.5164516920GI[t] -5817.46518820495M1[t] -8727.55493550756M2[t] -17855.7124462563M3[t] -22971.8113494768M4[t] -31496.9102526974M5[t] -27858.5M6[t] + 25881.4505483898M7[t] + 34141.0494516103M8[t] + 27216.5M9[t] + 13315.1758225846M10[t] -3447.83333333331M11[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)	594927.970623835	16314.907727	36.4653	0	0
GI	-17243.5164516920	3888.146543	-4.4349	4.1e-05	2e-05
M1	-5817.46518820495	19484.209737	-0.2986	0.766315	0.383157
M2	-8727.55493550756	19451.746008	-0.4487	0.655308	0.327654
M3	-17855.7124462563	19441.056722	-0.9185	0.362121	0.18106
M4	-22971.8113494768	19429.821239	-1.1823	0.241831	0.120916
M5	-31496.9102526974	19422.039021	-1.6217	0.110198	0.055099
M6	-27858.5	19416.741005	-1.4348	0.156633	0.078317
M7	25881.4505483898	19417.17355	1.3329	0.187684	0.093842
M8	34141.0494516103	19417.17355	1.7583	0.083884	0.041942
M9	27216.5	19416.741005	1.4017	0.166243	0.083122
M10	13315.1758225846	19417.714218	0.6857	0.495573	0.247787
M11	-3447.83333333331	19416.741005	-0.1776	0.859669	0.429834

Multiple Linear Regression - Regression Statistics
Multiple R	0.662414641170354
R-squared	0.438793156836848
Adjusted R-squared	0.324649392125699
F-TEST (value)	3.84421486313555
F-TEST (DF numerator)	12
F-TEST (DF denominator)	59
p-value	0.000242570584629398
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	33630.7819379067
Sum Squared Residuals	66730740131.5468

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	519164	573591.340629107	-54427.3406291072
2	517009	563783.844301128	-46774.844301128
3	509933	552931.33514521	-42998.3351452101
4	509127	546090.88459682	-36963.8845968203
5	500857	544463.192274277	-43606.1922742765
6	506971	539479.844301128	-32508.844301128
7	569323	594944.146494687	-25621.1464946869
8	579714	601479.393752738	-21765.3937527382
9	577992	592830.492655959	-14838.4926559588
10	565464	580653.520123713	-15189.5201237126
11	547344	562166.159322625	-14822.1593226254
12	554788	567338.344301128	-12550.3443011280
13	562325	561520.879112923	804.120887076976
14	560854	563783.844301128	-2929.84430112799
15	555332	558104.390080718	-2772.39008071764
16	543599	544366.532951651	-767.532951651121
17	536662	530668.379112923	5993.62088707702
18	542722	539479.844301128	3242.15569887204
19	593530	591495.443204349	2034.55679565148
20	610763	601479.393752738	9283.60624726175
21	612613	598003.547591466	14609.4524085336
22	611324	572031.761897867	39292.2381021334
23	594167	558717.456032287	35449.5439677129
24	595454	565613.992655959	29840.0073440412
25	590865	558072.175822585	32792.8241774154
26	589379	551713.382784944	37665.6172150564
27	584428	533963.467048349	50464.5329516511
28	573100	535744.774725805	37355.2252741949
29	567456	527219.675822585	40236.3241774154
30	569028	527409.382784944	41618.6172150564
31	620735	579424.981688164	41310.0183118359
32	628884	587684.580591385	41199.4194086153
33	628232	582484.382784944	45747.6172150564
34	612117	578929.168478543	33187.8315214566
35	595404	556993.104387118	38410.8956128821
36	597141	555267.882784944	41873.1172150564
37	593408	554623.472532246	38784.5274677538
38	590072	551713.382784944	38358.6172150564
39	579799	554655.686790379	25143.3132096207
40	574205	542642.181306482	31562.8186935181
41	572775	530668.379112923	42106.620887077
42	572942	537755.492655959	35186.5073440412
43	619567	593219.794849518	26347.2051504823
44	625809	599755.042107569	26053.9578924309
45	619916	591106.14101079	28809.8589892104
46	587625	575480.465188205	12144.534811795
47	565742	558717.456032287	7024.54396771293
48	557274	562165.28936562	-4891.28936562038
49	560576	554623.472532246	5952.52746775376
50	548854	549989.031139774	-1135.03113977442
51	531673	544309.576919364	-12636.5769193641
52	525919	539193.478016144	-13274.4780161435
53	511038	541014.488983938	-29976.4889839382
54	498662	544652.899236636	-45990.8992366356
55	555362	596668.498139856	-41306.4981398561
56	564591	608376.800333415	-43785.800333415
57	541657	599727.899236636	-58070.8992366356
58	527070	577204.816833374	-50134.8168333742
59	509846	553544.40109678	-43698.4010967795
60	514258	550094.827849436	-35836.827849436
61	516922	540828.659370893	-23906.6593708927
62	507561	532745.514688083	-25184.5146880825
63	492622	509822.54401598	-17200.5440159802
64	490243	508155.148403098	-17912.148403098
65	469357	484110.884693355	-14753.8846933547
66	477580	479127.536720206	-1547.53672020610
67	528379	531143.135623427	-2764.13562342665
68	533590	544575.789462155	-10985.7894621548
69	517945	534202.536720206	-16257.5367202061
70	506174	525474.267478298	-19300.2674782983
71	501866	524230.423128903	-22364.4231289031
72	516141	534575.663042913	-18434.6630429132

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.572496107531787	0.855007784936426	0.427503892468213
17	0.40127035712151	0.80254071424302	0.59872964287849
18	0.339616141478574	0.679232282957149	0.660383858521426
19	0.233073132336997	0.466146264673994	0.766926867663003
20	0.182826862237652	0.365653724475304	0.817173137762348
21	0.183636720801065	0.367273441602129	0.816363279198935
22	0.148430734473538	0.296861468947077	0.851569265526462
23	0.137543387395877	0.275086774791753	0.862456612604124
24	0.120425356561328	0.240850713122656	0.879574643438672
25	0.0892554663775737	0.178510932755147	0.910744533622426
26	0.0599547226007006	0.119909445201401	0.9400452773993
27	0.0481350301456474	0.0962700602912947	0.951864969854353
28	0.0348583912307602	0.0697167824615204	0.96514160876924
29	0.0249901269681397	0.0499802539362793	0.97500987303186
30	0.0170973336330609	0.0341946672661217	0.98290266636694
31	0.0119985236334213	0.0239970472668426	0.988001476366579
32	0.00921080219813615	0.0184216043962723	0.990789197801864
33	0.00809231539069898	0.0161846307813980	0.9919076846093
34	0.00972192524687258	0.0194438504937452	0.990278074753127
35	0.00954515725709607	0.0190903145141921	0.990454842742904
36	0.0107120186783895	0.0214240373567791	0.98928798132161
37	0.00885559746669698	0.0177111949333940	0.991144402533303
38	0.0082580264349071	0.0165160528698142	0.991741973565093
39	0.0181449161806878	0.0362898323613757	0.981855083819312
40	0.0250016087409034	0.0500032174818067	0.974998391259097
41	0.0454198579564544	0.0908397159129088	0.954580142043546
42	0.097041000192805	0.19408200038561	0.902958999807195
43	0.159283683380409	0.318567366760818	0.840716316619591
44	0.258854817159789	0.517709634319578	0.741145182840211
45	0.559551157696664	0.880897684606672	0.440448842303336
46	0.778806287738326	0.442387424523347	0.221193712261674
47	0.92395738964172	0.152085220716559	0.0760426103582796
48	0.95776232282211	0.084475354355779	0.0422376771778895
49	0.984318469369336	0.0313630612613283	0.0156815306306641
50	0.996211872145127	0.0075762557097451	0.00378812785487255
51	0.99824876975988	0.00350246048024043	0.00175123024012022
52	0.999485147549233	0.00102970490153403	0.000514852450767013
53	0.9999430492383	0.000113901523398892	5.69507616994458e-05
54	0.99975419597473	0.000491608050540802	0.000245804025270401
55	0.998388508685819	0.00322298262836231	0.00161149131418115
56	0.995462378816467	0.00907524236706652	0.00453762118353326

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	7	0.170731707317073	NOK
5% type I error level	19	0.463414634146341	NOK
10% type I error level	24	0.585365853658537	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/10v9841258743427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/10v9841258743427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/1bwta1258743427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/1bwta1258743427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/2f5r11258743427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/2f5r11258743427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/3kveg1258743427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/3kveg1258743427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/4em4y1258743427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/4em4y1258743427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/5h01p1258743427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/5h01p1258743427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/6y2nc1258743427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/6y2nc1258743427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/7shbq1258743427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/7shbq1258743427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/8go2c1258743427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/8go2c1258743427.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/9fz2t1258743427.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743452k9pm3n9hm4s2rqa/9fz2t1258743427.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal 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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