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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 11:59:51 -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/t1258743615mjo8lezj95pz77n.htm/, Retrieved Fri, 20 Nov 2009 20:00:27 +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/t1258743615mjo8lezj95pz77n.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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

TWIB[t] = + 598405.587863502 -13369.6576865744GI[t] -7413.8812149598M1[t] -10484.0553473849M2[t] -19514.0399922482M3[t] -24596.5303423353M4[t] -33088.0206924225M5[t] -29609.6948248475M6[t] + 24292.9929021796M7[t] + 33102.7148605492M8[t] + 26340.9025875762M9[t] + 12537.7512760462M10[t] -3739.69913747457M11[t] -291.865804141256t + 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)	598405.587863502	16560.028356	36.1355	0	0
GI	-13369.6576865744	5163.836716	-2.5891	0.012145	0.006073
M1	-7413.8812149598	19487.000702	-0.3805	0.704999	0.3525
M2	-10484.0553473849	19465.381514	-0.5386	0.592224	0.296112
M3	-19514.0399922482	19448.07854	-1.0034	0.31984	0.15992
M4	-24596.5303423353	19434.704041	-1.2656	0.210717	0.105359
M5	-33088.0206924225	19424.80765	-1.7034	0.093848	0.046924
M6	-29609.6948248475	19430.202191	-1.5239	0.132968	0.066484
M7	24292.9929021796	19419.798592	1.2509	0.215979	0.10799
M8	33102.7148605492	19390.963664	1.7071	0.093148	0.046574
M9	26340.9025875762	19384.311827	1.3589	0.179447	0.089723
M10	12537.7512760462	19382.040053	0.6469	0.520265	0.260132
M11	-3739.69913747457	19370.693803	-0.1931	0.847587	0.423793
t	-291.865804141256	256.847858	-1.1363	0.260489	0.130244

Multiple Linear Regression - Regression Statistics
Multiple R	0.67157671481839
R-squared	0.451015283886261
Adjusted R-squared	0.327966985446975
F-TEST (value)	3.66535165139889
F-TEST (DF numerator)	13
F-TEST (DF denominator)	58
p-value	0.000304387502449988
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	33548.0762906503
Sum Squared Residuals	65277458522.591

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	519164	578667.148926483	-59503.1489264833
2	517009	569957.245915287	-52948.2459152873
3	509933	559298.429697625	-49365.4296976253
4	509127	552587.107774739	-43460.1077747394
5	500857	549151.614695141	-48294.6146951408
6	506971	545653.245915287	-38682.2459152873
7	569323	600601.03360683	-31278.0336068305
8	579714	607781.923992401	-28067.9239924015
9	577992	599391.28014663	-21399.2801466299
10	565464	586633.228799616	-21169.2287996160
11	547344	568726.946813297	-21382.9468132965
12	554788	573511.745915287	-18723.7459152873
13	562325	565805.998896186	-3480.99889618625
14	560854	566454.856265592	-5600.85626559224
15	555332	559806.937353902	-4474.93735390253
16	543599	547747.752356387	-4148.75235638693
17	536662	534953.498896186	1708.50110381378
18	542722	542150.856265592	571.14373440777
19	593530	594424.71241982	-894.712419820626
20	610763	604279.534342706	6483.46565729357
21	612613	599899.787802907	12713.2121970929
22	611324	576446.010306634	34877.9896933663
23	594167	562550.625626287	31616.3743737134
24	595454	568672.390496935	26781.6095030652
25	590865	559629.677709176	31235.3222908237
26	589379	553593.706235295	35785.2937647049
27	584428	537587.026943003	46840.9730569968
28	573100	537560.533863405	35539.4661365954
29	567456	528777.177709176	38678.8222908237
30	569028	529289.706235295	39738.2937647049
31	620735	581563.562389523	39171.4376104765
32	628884	590081.418543752	38802.5814562482
33	628232	584364.706235295	43867.2937647049
34	612117	578291.483731568	33825.5162684316
35	595404	557711.270207934	37692.7297920659
36	597141	557148.206235295	39992.7937647050
37	593408	553453.356522166	39954.6434778337
38	590072	550091.3165856	39980.6834144
39	579799	550128.226517197	29670.7734828025
40	574205	539406.007288339	34798.9927116607
41	572775	527948.719596796	44826.2804032039
42	572942	533809.111197545	39132.8888024554
43	619567	588756.898889088	30810.1011109121
44	625809	595937.789274659	29871.2107253412
45	619916	587547.145428887	32368.8545711128
46	587625	572115.162544559	15509.8374554415
47	565742	555545.846326896	10196.1536731036
48	557274	558993.67966023	-1719.67966022974
49	560576	549950.966872471	10625.0331275287
50	548854	545251.961167248	3602.03883275252
51	531673	538604.042255558	-6931.04225555776
52	525919	533229.686101329	-7310.68610132938
53	511038	532468.124559046	-21430.1245590457
54	498662	535654.584622479	-36992.5846224793
55	555362	587928.440776708	-32566.4407767077
56	564591	599120.228468251	-34529.228468251
57	541657	590729.584622479	-49072.5846224793
58	527070	569949.738663521	-42879.7386635209
59	509846	548032.559371229	-38186.559371229
60	514258	546132.529629933	-31874.5296299325
61	516922	535752.851073517	-18830.8510735166
62	507561	528379.913830978	-20818.9138309780
63	492622	508362.337232714	-15740.3372327138
64	490243	505661.9126158	-15418.9126158003
65	469357	484845.864543655	-15488.8645436549
66	477580	481347.495763801	-3767.49576380139
67	528379	533621.35191803	-5242.35191802978
68	533590	546150.10537823	-12560.1053782305
69	517945	536422.495763801	-18477.4957638014
70	506174	526338.375954102	-20164.3759541025
71	501866	521801.751654357	-19935.7516543574
72	516141	530597.44806232	-14456.4480623205

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.00808724454638726	0.0161744890927745	0.991912755453613
18	0.00265647487573816	0.00531294975147632	0.997343525124262
19	0.00803680610919854	0.0160736122183971	0.991963193890801
20	0.00465637896443368	0.00931275792886736	0.995343621035566
21	0.00201115553832314	0.00402231107664628	0.997988844461677
22	0.00125395422729176	0.00250790845458352	0.998746045772708
23	0.000889657217519259	0.00177931443503852	0.99911034278248
24	0.000417282923170164	0.000834565846340328	0.99958271707683
25	0.000351472126253355	0.00070294425250671	0.999648527873747
26	0.000215057191316989	0.000430114382633979	0.999784942808683
27	8.63159391210599e-05	0.000172631878242120	0.999913684060879
28	8.79361843314385e-05	0.000175872368662877	0.999912063815669
29	5.57114321968692e-05	0.000111422864393738	0.999944288567803
30	5.95757155782346e-05	0.000119151431156469	0.999940424284422
31	0.000136818015193506	0.000273636030387012	0.999863181984807
32	0.000741355429419976	0.00148271085883995	0.99925864457058
33	0.00159449304462929	0.00318898608925858	0.99840550695537
34	0.0139928680402821	0.0279857360805642	0.986007131959718
35	0.0311537856576396	0.0623075713152792	0.96884621434236
36	0.084820634428551	0.169641268857102	0.915179365571449
37	0.135340174813374	0.270680349626749	0.864659825186626
38	0.161773549143238	0.323547098286475	0.838226450856762
39	0.147902150490534	0.295804300981069	0.852097849509466
40	0.122007404304796	0.244014808609592	0.877992595695204
41	0.104819576594167	0.209639153188335	0.895180423405833
42	0.112028532744325	0.22405706548865	0.887971467255675
43	0.117451605605601	0.234903211211202	0.882548394394399
44	0.138952284922714	0.277904569845428	0.861047715077286
45	0.444887139100843	0.889774278201685	0.555112860899157
46	0.781547501187898	0.436904997624205	0.218452498812102
47	0.892270791086147	0.215458417827707	0.107729208913853
48	0.924991186875362	0.150017626249276	0.0750088131246382
49	0.955766492580639	0.0884670148387224	0.0442335074193612
50	0.98150809729385	0.0369838054123025	0.0184919027061512
51	0.991434836389865	0.0171303272202693	0.00856516361013466
52	0.998117415635155	0.00376516872969093	0.00188258436484547
53	0.999905850238906	0.000188299522188247	9.41497610941233e-05
54	0.999812012393342	0.000375975213316028	0.000187987606658014
55	0.998168153449488	0.00366369310102372	0.00183184655051186

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	19	0.487179487179487	NOK
5% type I error level	24	0.615384615384615	NOK
10% type I error level	26	0.666666666666667	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/10u6zb1258743587.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/10u6zb1258743587.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/1177e1258743587.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/1177e1258743587.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/2p8tx1258743587.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/2p8tx1258743587.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/3fzi11258743587.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/3fzi11258743587.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/41ja81258743587.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/41ja81258743587.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/5n7fg1258743587.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/5n7fg1258743587.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/6h3o81258743587.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/6h3o81258743587.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/7079b1258743587.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/7079b1258743587.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/82nrn1258743587.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/82nrn1258743587.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/936ve1258743587.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743615mjo8lezj95pz77n/936ve1258743587.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 = 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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