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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: Tue, 17 Nov 2009 09:45:02 -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/17/t1258476333nfg1dkggle8wbki.htm/, Retrieved Tue, 17 Nov 2009 17:45:45 +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/17/t1258476333nfg1dkggle8wbki.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 «

20604.6 2.05 18714.9 2.03 18492.6 2.04 18183.6 2.03 19435.1 2.01 22686.8 2.01 20396.7 2.01 19233.6 2.01 22751 2.01 19864 2.01 17165.4 2.02 22309.7 2.02 21786.3 2.03 21927.6 2.05 20957.9 2.08 19726 2.07 21315.7 2.06 24771.5 2.05 22592.4 2.05 21942.1 2.05 23973.7 2.05 20815.7 2.05 19931.4 2.06 24436.8 2.06 22838.7 2.07 24465.3 2.07 23007.3 2.3 22720.8 2.31 23045.7 2.31 27198.5 2.53 22401.9 2.58 25122.7 2.59 26100.5 2.73 22904.9 2.82 22040.4 3 25981.5 3.04 26157.1 3.23 25975.4 3.32 22589.8 3.49 25370.4 3.57 25091.1 3.56 28760.9 3.72 24325.9 3.82 25821.7 3.82 27645.7 3.98 26296.9 4.06 24141.5 4.08 27268.1 4.19 29060.3 4.16 28226.4 4.17 23268.5 4.21 26938.2 4.21 27217.5 4.17 27540.5 4.19 29167.6 4.25 26671.5 4.25 30184 4.2 28422.3 4.33 23774.3 4.41 29601 4.56 28523.6 5.18 23622 3.42 21320.3 2.71 20423.6 2.29 21174.9 2 23050.2 1.64 21202.9 1.3 20476.4 1.08 23173.3 1 22468 1 19842.7 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	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 18648.2462474919 + 2290.85499448899X[t] -967.28049696427M1[t] -1339.97728182231M2[t] -3468.0278403669M3[t] -2713.36129902170M4[t] -1919.19190769488M5[t] + 857.42048399934M6[t] -1413.11099112007M7[t] -1469.49773297962M8[t] + 892.29470884319M9[t] -1398.31470754792M10[t] -3825.5407906057M11[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)	18648.2462474919	877.446114	21.2529	0	0
X	2290.85499448899	175.512018	13.0524	0	0
M1	-967.28049696427	917.960384	-1.0537	0.296378	0.148189
M2	-1339.97728182231	919.744321	-1.4569	0.150536	0.075268
M3	-3468.0278403669	920.193354	-3.7688	0.000386	0.000193
M4	-2713.36129902170	920.970554	-2.9462	0.004626	0.002313
M5	-1919.19190769488	921.915092	-2.0817	0.041789	0.020894
M6	857.42048399934	921.83381	0.9301	0.356162	0.178081
M7	-1413.11099112007	922.192012	-1.5323	0.130876	0.065438
M8	-1469.49773297962	922.803455	-1.5924	0.116725	0.058362
M9	892.29470884319	922.305354	0.9675	0.33733	0.168665
M10	-1398.31470754792	921.491162	-1.5174	0.134586	0.067293
M11	-3825.5407906057	920.759892	-4.1548	0.000108	5.4e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.897820391166252
R-squared	0.80608145479392
Adjusted R-squared	0.765960376475422
F-TEST (value)	20.0912210881992
F-TEST (DF numerator)	12
F-TEST (DF denominator)	58
p-value	2.22044604925031e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1515.88087916464
Sum Squared Residuals	133277900.709384

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	20604.6	22377.2184892301	-1772.61848923014
2	18714.9	21958.7046044823	-3243.80460448229
3	18492.6	19853.5625958826	-1360.96259588259
4	18183.6	20585.3205872829	-2401.72058728289
5	19435.1	21333.6728787199	-1898.57287871993
6	22686.8	24110.2852704142	-1423.48527041415
7	20396.7	21839.7537952947	-1443.05379529475
8	19233.6	21783.3670534352	-2549.76705343520
9	22751	24145.159495258	-1394.15949525801
10	19864	21854.5500788669	-1990.55007886689
11	17165.4	19450.232545754	-2284.83254575399
12	22309.7	23275.7733363597	-966.0733363597
13	21786.3	22331.4013893403	-545.101389340324
14	21927.6	22004.5217043721	-76.9217043720664
15	20957.9	19945.1967956621	1012.70320433785
16	19726	20676.9547870624	-950.954787062448
17	21315.7	21448.2156284444	-132.515628444379
18	24771.5	24201.9194701937	569.580529806288
19	22592.4	21931.3879950743	661.012004925695
20	21942.1	21875.0012532148	67.0987467852443
21	23973.7	24236.7936950376	-263.093695037566
22	20815.7	21946.1842786465	-1130.48427864645
23	19931.4	19541.8667455336	389.533254466442
24	24436.8	23367.4075361393	1069.39246386074
25	22838.7	22423.0355891199	415.664410880116
26	24465.3	22050.3388042618	2414.96119573816
27	23007.3	20449.1848944497	2558.11510555027
28	22720.8	21226.7599857398	1494.04001426019
29	23045.7	22020.9293770666	1024.77062293337
30	27198.5	25301.5298675484	1896.97013245157
31	22401.9	23145.5411421535	-743.641142153472
32	25122.7	23112.0629502388	2010.63704976119
33	26100.5	25794.5750912901	305.924908709919
34	22904.9	23710.142624403	-805.242624402973
35	22040.4	21695.2704403532	345.129559646789
36	25981.5	25612.4454307385	369.054569261526
37	26157.1	25080.4273827271	1076.67261727288
38	25975.4	24913.9075473731	1061.49245262692
39	22589.8	23175.3023378916	-585.502337891627
40	25370.4	24113.2372787959	1257.16272120406
41	25091.1	24884.4981201779	206.60187982213
42	28760.9	28027.6473109903	733.252689009671
43	24325.9	25986.2013353198	-1660.30133531982
44	25821.7	25929.8145934603	-108.11459346027
45	27645.7	28658.1438344013	-1012.44383440132
46	26296.9	26550.8028175693	-253.902817569323
47	24141.5	24169.3938344013	-27.893834401323
48	27268.1	28246.9286744008	-978.828674400817
49	29060.3	27210.9225276019	1849.37747239812
50	28226.4	26861.1342926887	1365.26570731127
51	23268.5	24824.7179339237	-1556.2179339237
52	26938.2	25579.3844752689	1358.81552473111
53	27217.5	26281.9196668162	935.580333183847
54	27540.5	29104.3491584002	-1563.84915840016
55	29167.6	26971.2689829501	2196.33101704991
56	26671.5	26914.8822410905	-243.382241090538
57	30184	29162.1319331889	1021.86806681110
58	28422.3	27169.3336660814	1252.96633391865
59	23774.3	24925.3759825827	-1151.07598258269
60	29601	29094.5450223617	506.454977638259
61	28523.6	29547.5946219807	-1023.99462198065
62	23622	25142.993046822	-1520.99304682198
63	21320.3	21388.4354421902	-68.1354421902131
64	20423.6	21180.9428858500	-757.342885850028
65	21174.9	21310.7643287750	-135.864328775039
66	23050.2	23262.6689224532	-212.468922453224
67	21202.9	20213.2467492076	989.653250792438
68	20476.4	19652.8719085604	823.52809143957
69	23173.3	21831.3959508241	1341.90404917588
70	22468	19540.786534433	2927.21346556699
71	19842.7	17113.5604513752	2729.13954862477

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.6555045126026	0.688990974794801	0.344495487397400
17	0.507254494926638	0.985491010146723	0.492745505073362
18	0.358374927054611	0.716749854109223	0.641625072945389
19	0.238283460057394	0.476566920114788	0.761716539942606
20	0.167066557498920	0.334133114997839	0.83293344250108
21	0.117168436377505	0.234336872755009	0.882831563622495
22	0.103033498972851	0.206066997945701	0.89696650102715
23	0.0739711620773857	0.147942324154771	0.926028837922614
24	0.0426234198037614	0.0852468396075228	0.957376580196239
25	0.0254775432372003	0.0509550864744007	0.9745224567628
26	0.108606472722652	0.217212945445304	0.891393527277348
27	0.64414298383893	0.71171403232214	0.35585701616107
28	0.605426113047012	0.789147773905976	0.394573886952988
29	0.600169597764822	0.799660804470356	0.399830402235178
30	0.723125546988867	0.553748906022265	0.276874453011133
31	0.861579227239102	0.276841545521797	0.138420772760898
32	0.840829472824597	0.318341054350806	0.159170527175403
33	0.824621992274196	0.350756015451608	0.175378007725804
34	0.862412404946463	0.275175190107075	0.137587595053537
35	0.828094244722	0.343811510556	0.171905755278
36	0.802977530232762	0.394044939534477	0.197022469767239
37	0.742000182953836	0.515999634092328	0.257999817046164
38	0.691137925192348	0.617724149615305	0.308862074807653
39	0.720809221594012	0.558381556811976	0.279190778405988
40	0.661853629389112	0.676292741221775	0.338146370610888
41	0.588634295580508	0.822731408838984	0.411365704419492
42	0.580149871453007	0.839700257093987	0.419850128546993
43	0.69883569401715	0.602328611965699	0.301164305982850
44	0.621218817873769	0.757562364252461	0.378781182126231
45	0.610117013202625	0.77976597359475	0.389882986797375
46	0.59940179099337	0.801196418013259	0.400598209006630
47	0.506124940554518	0.987750118890965	0.493875059445482
48	0.478701186862331	0.957402373724662	0.521298813137669
49	0.52184190894556	0.95631618210888	0.47815809105444
50	0.63408499557356	0.731830008852879	0.365915004426439
51	0.594538197987315	0.81092360402537	0.405461802012685
52	0.679821391552034	0.640357216895932	0.320178608447966
53	0.666603614861295	0.666792770277409	0.333396385138704
54	0.551662872235085	0.896674255529831	0.448337127764915
55	0.684090775684097	0.631818448631806	0.315909224315903

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	2	0.05	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/103wbg1258476296.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/103wbg1258476296.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/1d3ke1258476296.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/1d3ke1258476296.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/2x3vf1258476296.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/2x3vf1258476296.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/3nhoo1258476296.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/3nhoo1258476296.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/4a0hs1258476296.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/4a0hs1258476296.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/5o9ju1258476296.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/5o9ju1258476296.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/6exmm1258476296.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/6exmm1258476296.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/7oxvl1258476296.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/7oxvl1258476296.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/8f9mw1258476296.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/8f9mw1258476296.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/9kz9t1258476296.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476333nfg1dkggle8wbki/9kz9t1258476296.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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