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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 09:47:07 -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/t12584764532ydpyv80hluodqs.htm/, Retrieved Tue, 17 Nov 2009 17:47:44 +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/t12584764532ydpyv80hluodqs.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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 18289.7802466385 + 2000.13295416083X[t] -805.032576467682M1[t] -1293.75184128471M2[t] -3468.53612684340M3[t] -2766.41775331888M4[t] -2025.76560328049M5[t] + 716.711016480509M6[t] -1595.7088183142M7[t] -1697.8602137201M8[t] + 636.579970443811M9[t] -1675.08272606575M10[t] -4123.36208924199M11[t] + 35.5893821348614t + 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)	18289.7802466385	781.621344	23.3998	0	0
X	2000.13295416083	170.618446	11.7228	0	0
M1	-805.032576467682	813.59104	-0.9895	0.32661	0.163305
M2	-1293.75184128471	814.295708	-1.5888	0.117638	0.058819
M3	-3468.53612684340	814.616086	-4.2579	7.8e-05	3.9e-05
M4	-2766.41775331888	815.4056	-3.3927	0.001264	0.000632
M5	-2025.76560328049	816.549286	-2.4809	0.016081	0.00804
M6	716.711016480509	816.781236	0.8775	0.383908	0.191954
M7	-1595.7088183142	817.58516	-1.9517	0.055889	0.027945
M8	-1697.8602137201	818.801181	-2.0736	0.042647	0.021323
M9	636.579970443811	818.836735	0.7774	0.440126	0.220063
M10	-1675.08272606575	818.520758	-2.0465	0.045332	0.022666
M11	-4123.36208924199	818.310266	-5.0389	5e-06	3e-06
t	35.5893821348614	8.629595	4.1241	0.000122	6.1e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.922305259300235
R-squared	0.850646991332874
Adjusted R-squared	0.81658402444388
F-TEST (value)	24.9727803836058
F-TEST (DF numerator)	13
F-TEST (DF denominator)	57
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1341.95811085843
Sum Squared Residuals	102648539.564028

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	20604.6	21620.6096083354	-1016.00960833543
2	18714.9	21127.4770665700	-2412.57706657003
3	18492.6	19008.2834926878	-515.683492687790
4	18183.6	19725.9899188056	-1542.38991880557
5	19435.1	20462.2287918956	-1027.12879189560
6	22686.8	23240.2947937915	-553.494793791462
7	20396.7	20963.4643411316	-566.764341131614
8	19233.6	20896.9023278606	-1663.30232786058
9	22751	23266.9318941594	-515.931894159351
10	19864	20990.8585797846	-1126.85857978465
11	17165.4	18598.1699282849	-1432.76992828487
12	22309.7	22757.1213996617	-447.421399661726
13	21786.3	22007.6795348705	-221.379534870516
14	21927.6	21594.5523112716	333.047688728432
15	20957.9	19515.3613964726	1442.53860352744
16	19726	20233.0678225903	-507.067822590333
17	21315.7	20989.3080252220	326.391974778024
18	24771.5	23747.3726975762	1024.12730242377
19	22592.4	21470.5422449164	1121.85775508362
20	21942.1	21403.9802316453	538.119768354654
21	23973.7	23774.0097979441	199.69020205588
22	20815.7	21497.9364835694	-682.236483569412
23	19931.4	19105.2478320697	826.152167930353
24	24436.8	23264.1993034465	1172.6006965535
25	22838.7	22514.7574386553	323.942561344714
26	24465.3	22061.6275559731	2403.67244402688
27	23007.3	20382.4632320063	2624.83676799372
28	22720.8	21140.1723172073	1580.62768279273
29	23045.7	21916.4138493805	1129.28615061948
30	27198.5	25134.5091011918	2063.99089880823
31	22401.9	22957.6852962400	-555.785296239958
32	25122.7	22911.1246125105	2211.57538748947
33	26100.5	25561.1727923918	539.327207608178
34	22904.9	23465.1114438916	-560.211443891587
35	22040.4	21412.4453945992	627.954605400836
36	25981.5	25651.4021841424	330.097815857552
37	26157.1	25261.9842511002	895.115748899812
38	25975.4	24988.8663342925	986.533665707505
39	22589.8	23189.694033076	-599.894033076004
40	25370.4	24087.4124250683	1282.98757493175
41	25091.1	24843.6526276999	247.447372300104
42	28760.9	27941.7399022615	819.16009773851
43	24325.9	25864.9227450177	-1539.02274501772
44	25821.7	25798.3607317467	23.3392682533148
45	27645.7	28488.4115707112	-842.711570711195
46	26296.9	26372.3488926694	-75.448892669352
47	24141.5	23999.6615707112	141.838429288803
48	27268.1	28378.6276670457	-1110.52766704574
49	29060.3	27549.1804840881	1511.11951591190
50	28226.4	27116.0519309475	1110.34806905246
51	23268.5	25056.8623456901	-1788.36234569014
52	26938.2	25794.5701013495	1143.62989865048
53	27217.5	26490.8063153563	726.693684643663
54	27540.5	29308.8749763354	-1768.37497633542
55	29167.6	27152.0525009252	2015.54749907478
56	26671.5	27085.4904876542	-413.99048765418
57	30184	29355.5134062449	828.486593755084
58	28422.3	27339.4573759111	1082.84262408888
59	23774.3	25086.7780312026	-1312.47803120261
60	29601	29545.7494457036	55.2505542964189
61	28523.6	30016.3886829505	-1492.78868295048
62	23622	26043.0248009453	-2421.02480094525
63	21320.3	22483.7355000672	-1163.43550006723
64	20423.6	22381.3874149791	-1957.78741497906
65	21174.9	22577.5903904457	-1402.69039044567
66	23050.2	24635.6085288436	-1585.40852884364
67	21202.9	21678.7328717691	-475.832871769106
68	20476.4	21172.1416085827	-695.741608582684
69	23173.3	23382.1605385486	-208.860538548597
70	22468	21106.0872241739	1361.91277582611
71	19842.7	18693.3972431325	1149.30275686749

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.141341695478432	0.282683390956865	0.858658304521568
18	0.0555255593557262	0.111051118711452	0.944474440644274
19	0.0195993872174184	0.0391987744348368	0.980400612782582
20	0.00853280178546307	0.0170656035709261	0.991467198214537
21	0.00718712361761492	0.0143742472352298	0.992812876382385
22	0.00908101647598873	0.0181620329519775	0.990918983524011
23	0.00548813467461714	0.0109762693492343	0.994511865325383
24	0.00209996081533589	0.00419992163067178	0.997900039184664
25	0.00274418671868671	0.00548837343737342	0.997255813281313
26	0.00558610064245777	0.0111722012849155	0.994413899357542
27	0.00425406943389625	0.0085081388677925	0.995745930566104
28	0.00197716005370852	0.00395432010741705	0.998022839946291
29	0.00130457943738323	0.00260915887476646	0.998695420562617
30	0.000821703497963171	0.00164340699592634	0.999178296502037
31	0.0049031093394125	0.009806218678825	0.995096890660587
32	0.0126529023851807	0.0253058047703614	0.98734709761482
33	0.00694653017749355	0.0138930603549871	0.993053469822506
34	0.00670439327767234	0.0134087865553447	0.993295606722328
35	0.00410964517471463	0.00821929034942926	0.995890354825285
36	0.00216719760168686	0.00433439520337373	0.997832802398313
37	0.00115791538770798	0.00231583077541596	0.998842084612292
38	0.000593917036155262	0.00118783407231052	0.999406082963845
39	0.00186198498970845	0.0037239699794169	0.998138015010292
40	0.00231990357239066	0.00463980714478132	0.99768009642761
41	0.00113246703138667	0.00226493406277334	0.998867532968613
42	0.00111658678496811	0.00223317356993622	0.998883413215032
43	0.00288987472966119	0.00577974945932238	0.99711012527034
44	0.00141509503758232	0.00283019007516464	0.998584904962418
45	0.00133293714621784	0.00266587429243568	0.998667062853782
46	0.0044847236995452	0.0089694473990904	0.995515276300455
47	0.00414205619611726	0.00828411239223452	0.995857943803883
48	0.0303072946903831	0.0606145893807662	0.969692705309617
49	0.0332197331061497	0.0664394662122994	0.96678026689385
50	0.0218033341453527	0.0436066682907055	0.978196665854647
51	0.269396195411029	0.538792390822059	0.73060380458897
52	0.184041040209057	0.368082080418115	0.815958959790942
53	0.106558965108142	0.213117930216283	0.893441034891858
54	0.80862190969066	0.382756180618681	0.191378090309341

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	20	0.526315789473684	NOK
5% type I error level	30	0.789473684210526	NOK
10% type I error level	32	0.842105263157895	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/10x6kl1258476422.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/10x6kl1258476422.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/1hup41258476422.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/1hup41258476422.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/2ultp1258476422.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/2ultp1258476422.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/3sx641258476422.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/3sx641258476422.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/4avxo1258476422.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/4avxo1258476422.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/5w6ix1258476422.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/5w6ix1258476422.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/6ju8h1258476422.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/6ju8h1258476422.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/7mhra1258476422.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/7mhra1258476422.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/8dsdc1258476422.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/8dsdc1258476422.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/940ye1258476422.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584764532ydpyv80hluodqs/940ye1258476422.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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