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Paper Multiple regression (monthly dummies + trend)

*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: Sat, 04 Dec 2010 13:53:29 +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/04/t1291470725yzjitxpjtz8kr01.htm/, Retrieved Sat, 04 Dec 2010 14:52:15 +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/04/t1291470725yzjitxpjtz8kr01.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 «

1,79 194,9 1,95 195,5 2,26 196,0 2,04 196,2 2,16 196,2 2,75 196,2 2,79 196,2 2,88 197,0 3,36 197,7 2,97 198,0 3,10 198,2 2,49 198,5 2,2 198,6 2,25 199,5 2,09 200 2,79 201,3 3,14 202,2 2,93 202,9 2,65 203,5 2,67 203,5 2,26 204 2,35 204,1 2,13 204,3 2,18 204,5 2,9 204,8 2,63 205,1 2,67 205,7 1,81 206,5 1,33 206,9 0,88 207,1 1,28 207,8 1,26 208 1,26 208,5 1,29 208,6 1,1 209 1,37 209,1 1,21 209,7 1,74 209,8 1,76 209,9 1,48 210 1,04 210,8 1,62 211,4 1,49 211,7 1,79 212 1,8 212,2 1,58 212,4 1,86 212,9 1,74 213,4 1,59 213,7 1,26 214 1,13 214,3 1,92 214,8 2,61 215 2,26 215,9 2,41 216,4 2,26 216,9 2,03 217,2 2,86 217,5 2,55 217,9 2,27 218,1 2,26 218,6 2,57 218,9 3,07 219,3 2,76 220,4 2,51 220,9 2,87 221 3,14 221,8 3,11 222 3,16 222,2 2,47 222,5 2,57 222,9 2,89 223,1

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	113 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 34.2655743166752 -0.165157690441535X[t] -0.169194606178226M1[t] -0.0910606501698221M2[t] + 0.00598734433123115M3[t] + 0.0204107229500253M4[t] + 0.0301358968138449M5[t] + 0.119936519488922M6[t] + 0.209080988193435M7[t] + 0.233451803331717M8[t] + 0.217166464499433M9[t] + 0.128935549086204M10[t] + 0.0860589923985128M11[t] + 0.0656817483422296t + 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)	34.2655743166752	30.273129	1.1319	0.262343	0.131171
X	-0.165157690441535	0.155704	-1.0607	0.293215	0.146607
M1	-0.169194606178226	0.400678	-0.4223	0.674389	0.337194
M2	-0.0910606501698221	0.400246	-0.2275	0.820826	0.410413
M3	0.00598734433123115	0.399872	0.015	0.988105	0.494052
M4	0.0204107229500253	0.40165	0.0508	0.959646	0.479823
M5	0.0301358968138449	0.4026	0.0749	0.940589	0.470295
M6	0.119936519488922	0.40274	0.2978	0.766919	0.383459
M7	0.209080988193435	0.40456	0.5168	0.607253	0.303627
M8	0.233451803331717	0.402817	0.5795	0.564464	0.282232
M9	0.217166464499433	0.402734	0.5392	0.591792	0.295896
M10	0.128935549086204	0.399511	0.3227	0.748058	0.374029
M11	0.0860589923985128	0.398957	0.2157	0.82997	0.414985
t	0.0656817483422296	0.062038	1.0587	0.294108	0.147054

Multiple Linear Regression - Regression Statistics
Multiple R	0.205311131411143
R-squared	0.0421526606813234
Adjusted R-squared	-0.172537260200449
F-TEST (value)	0.196342056991751
F-TEST (DF numerator)	13
F-TEST (DF denominator)	58
p-value	0.998747705775358
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.689831138014099
Sum Squared Residuals	27.6002859404819

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1.79	1.97282759178389	-0.182827591783894
2	1.95	2.01754868186965	-0.0675486818696511
3	2.26	2.09769957949216	0.162300420507844
4	2.04	2.14477316836487	-0.104773168364875
5	2.16	2.22018009057092	-0.0601800905709236
6	2.75	2.37566246158823	0.374337538411769
7	2.79	2.53048867863497	0.259511321365028
8	2.88	2.48841508976225	0.391584910237746
9	3.36	2.42220111596313	0.937798884036872
10	2.97	2.35010464175966	0.619895358240335
11	3.1	2.33987829532590	0.760121704674103
12	2.49	2.26995374413715	0.220046255862848
13	2.2	2.14992511725700	0.0500748827429965
14	2.25	2.14509890021025	0.104901099789747
15	2.09	2.22524979783277	-0.135249797832769
16	2.79	2.09064992721979	0.699350072780206
17	3.14	2.01741492802847	1.12258507197153
18	2.93	2.05728691573669	0.872713084263306
19	2.65	2.11301851851852	0.536981481481484
20	2.67	2.20307108199903	0.466928918000972
21	2.26	2.16988864628821	0.0901113537117932
22	2.35	2.13082371017305	0.219176289826946
23	2.13	2.12059736373928	0.00940263626071736
24	2.18	2.06718858159469	0.112811418405306
25	2.9	1.91412841662624	0.985871583373764
26	2.63	2.00839681384441	0.621603186155589
27	2.67	2.07203194242277	0.597968057577227
28	1.81	2.02001091703057	-0.210010917030567
29	1.33	2.02935476306	-0.699354763060001
30	0.88	2.151805595989	-1.27180559598900
31	1.28	2.19102142972667	-0.911021429726667
32	1.26	2.24804245511887	-0.988042455118874
33	1.26	2.21486001940805	-0.954860019408051
34	1.29	2.1757950832929	-0.8857950832929
35	1.1	2.13253719877082	-1.03253719877082
36	1.37	2.09564418567039	-0.725644185670386
37	1.21	1.89303671356947	-0.68303671356947
38	1.74	2.02033664887595	-0.280336648875946
39	1.76	2.16655062267508	-0.406550622675076
40	1.48	2.23013998059195	-0.750139980591947
41	1.04	2.17342075044477	-1.13342075044477
42	1.62	2.22980850719715	-0.609808507197153
43	1.49	2.33508741711144	-0.845087417111437
44	1.79	2.37559267345949	-0.585592673459486
45	1.8	2.39195754488113	-0.591957544881127
46	1.58	2.33637683972182	-0.756376839721818
47	1.86	2.27660318615559	-0.416603186155588
48	1.74	2.17364709687854	-0.433647096878537
49	1.59	2.02058693191008	-0.430586931910083
50	1.26	2.11485532912825	-0.854855329128254
51	1.13	2.22803776483907	-1.09803776483907
52	1.92	2.22556404657933	-0.30556404657933
53	2.61	2.26793943069707	0.342060569302926
54	2.26	2.274779880317	-0.0147798803169983
55	2.41	2.34702725214297	0.0629727478570277
56	2.26	2.35450097040272	-0.094500970402717
57	2.03	2.35435007278020	-0.324350072780205
58	2.86	2.28225359857674	0.577746401423257
59	2.55	2.23899571405467	0.311004285945334
60	2.27	2.18558693191008	0.0844130680899229
61	2.26	1.99949522885331	0.260504771146687
62	2.57	2.09376362607148	0.476236373928516
63	3.07	2.19043029273815	0.879569707261848
64	2.76	2.08886196021349	0.671138039786513
65	2.51	2.08169003719877	0.428309962801231
66	2.87	2.22065663917192	0.649343360828077
67	3.14	2.24335670386543	0.896643296134566
68	3.11	2.30037772925764	0.809622270742358
69	3.16	2.31674260067928	0.843257399320717
70	2.47	2.24464612647582	0.225353873524180
71	2.57	2.20138824195374	0.368611758046257
72	2.89	2.14797945980916	0.742020540190845

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0475668647276972	0.0951337294553944	0.952433135272303
18	0.0917543847318008	0.183508769463602	0.9082456152682
19	0.0913075949779908	0.182615189955982	0.90869240502201
20	0.0738785601296186	0.147757120259237	0.926121439870381
21	0.222683004298351	0.445366008596703	0.777316995701649
22	0.211915560868156	0.423831121736311	0.788084439131844
23	0.259345628220498	0.518691256440995	0.740654371779502
24	0.215421838900496	0.430843677800993	0.784578161099504
25	0.49161697354269	0.98323394708538	0.50838302645731
26	0.657537511677119	0.684924976645761	0.342462488322881
27	0.854740835009741	0.290518329980517	0.145259164990259
28	0.92884086945715	0.142318261085700	0.0711591305428499
29	0.984367796144413	0.0312644077111749	0.0156322038555874
30	0.998486974433686	0.00302605113262815	0.00151302556631408
31	0.998758119194305	0.00248376161139078	0.00124188080569539
32	0.99884619626418	0.00230760747164020	0.00115380373582010
33	0.998746459044235	0.002507081911531	0.0012535409557655
34	0.998170340408367	0.0036593191832652	0.0018296595916326
35	0.997594707726219	0.00481058454756239	0.00240529227378119
36	0.995582505015682	0.00883498996863683	0.00441749498431842
37	0.991812803555085	0.0163743928898297	0.00818719644491483
38	0.99287315830626	0.0142536833874802	0.00712684169374009
39	0.994708570059998	0.0105828598800037	0.00529142994000184
40	0.990114337423484	0.0197713251530311	0.00988566257651555
41	0.98628505111947	0.0274298977610586	0.0137149488805293
42	0.97712043573048	0.0457591285390394	0.0228795642695197
43	0.963967233047536	0.0720655339049281	0.0360327669524640
44	0.941961297772421	0.116077404455157	0.0580387022275786
45	0.911680739535312	0.176638520929375	0.0883192604646876
46	0.86581665732663	0.268366685346742	0.134183342673371
47	0.823412271722587	0.353175456554826	0.176587728277413
48	0.781670167023096	0.436659665953807	0.218329832976904
49	0.704421262954735	0.59115747409053	0.295578737045265
50	0.636820999421488	0.726358001157025	0.363179000578512
51	0.837330690471809	0.325338619056383	0.162669309528191
52	0.806364651472857	0.387270697054287	0.193635348527143
53	0.814676981703064	0.370646036593872	0.185323018296936
54	0.706042757504804	0.587914484990392	0.293957242495196
55	0.596362558545417	0.807274882909166	0.403637441454583

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	7	0.179487179487179	NOK
5% type I error level	14	0.358974358974359	NOK
10% type I error level	16	0.41025641025641	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/10brlk1291470695.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/10brlk1291470695.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/1bg4v1291470694.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/1bg4v1291470694.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/2bg4v1291470694.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/2bg4v1291470694.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/3eznb1291470695.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/3eznb1291470695.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/4eznb1291470695.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/4eznb1291470695.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/5eznb1291470695.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/5eznb1291470695.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/67rne1291470695.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/67rne1291470695.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/70i4z1291470695.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/70i4z1291470695.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/80i4z1291470695.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/80i4z1291470695.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/90i4z1291470695.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291470725yzjitxpjtz8kr01/90i4z1291470695.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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