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paper - 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: Tue, 21 Dec 2010 21:12:09 +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/21/t1292965800i8abhgoavucy65w.htm/, Retrieved Tue, 21 Dec 2010 22:10:11 +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/21/t1292965800i8abhgoavucy65w.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 «

99.2 96.7 101.0 99.0 98.1 100.1 631 923 -12 -10.8 654 294 -13 -12.2 671 833 -16 -14.1 586 840 -10 -15.2 600 969 -4 -15.8 625 568 -9 -15.8 558 110 -8 -14.9 630 577 -9 -12.6 628 654 -3 -9.9 603 184 -13 -7.8 656 255 -3 -6 600 730 -1 -5 670 326 -2 -4.5 678 423 0 -3.9 641 502 0 -2.9 625 311 -3 -1.5 628 177 0 -0.5 589 767 5 0 582 471 3 0.5 636 248 4 0.9 599 885 3 0.8 621 694 1 0.1 637 406 -1 -1 595 994 0 -2 696 308 -2 -3 674 201 -1 -3.7 648 861 2 -4.7 649 605 0 -6.4 672 392 -6 -7.5 598 396 -7 -7.8 613 177 -6 -7.7 638 104 -4 -6.6 615 632 -9 -4.2 634 465 -2 -2 638 686 -3 -0.7 604 243 2 0.1 706 669 3 0.9 677 185 1 2.1 644 328 0 3.5 644 825 1 4.9 605 707 1 5.7 600 136 3 6.2 612 166 5 6.5 599 659 5 6.5 634 210 4 6.3 618 234 11 6.2 613 576 8 6.4 627 200 -1 6.3 668 973 4 5.8 651 479 4 5.1 619 661 4 5.1 644 260 6 5.8 579 936 6 6.7 601 752 6 7.1 595 376 6 6.7 588 902 4 5.5 634 341 1 4.2 594 305 6 3 606 200 0 2.2 610 926 2 2 633 685 -2 1.8 639 696 0 1.8 659 451 etc...

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	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

Werkloosheid[t] = + 31.3387138937592 + 0.0155462258573491Consumenten[t] -0.0412319081503414Ondernemers[t] + 20.6242720552282M1[t] + 490.964313243321M2[t] + 602.654800306586M3[t] -10.2499624707208M4[t] + 9.92761684711484M5[t] + 515.658777319681M6[t] + 604.420100331333M7[t] -6.33585887284848M8[t] + 11.2580469861704M9[t] + 618.072553059809M10[t] + 610.932913672097M11[t] -0.395084604896918t + 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)	31.3387138937592	126.29072	0.2481	0.804737	0.402368
Consumenten	0.0155462258573491	0.133146	0.1168	0.907379	0.45369
Ondernemers	-0.0412319081503414	0.119592	-0.3448	0.731286	0.365643
M1	20.6242720552282	112.540183	0.1833	0.855115	0.427558
M2	490.964313243321	133.438467	3.6793	0.000452	0.000226
M3	602.654800306586	106.083276	5.681	0	0
M4	-10.2499624707208	81.586061	-0.1256	0.900377	0.450188
M5	9.92761684711484	115.414027	0.086	0.931695	0.465847
M6	515.658777319681	137.267314	3.7566	0.00035	0.000175
M7	604.420100331333	104.20952	5.8	0	0
M8	-6.33585887284848	80.277466	-0.0789	0.937315	0.468657
M9	11.2580469861704	115.314063	0.0976	0.922502	0.461251
M10	618.072553059809	137.278901	4.5023	2.6e-05	1.3e-05
M11	610.932913672097	113.521683	5.3816	1e-06	0
t	-0.395084604896918	0.662975	-0.5959	0.553119	0.27656

Multiple Linear Regression - Regression Statistics
Multiple R	0.910034073036408
R-squared	0.828162014087235
Adjusted R-squared	0.794278467569225
F-TEST (value)	24.4414206655447
F-TEST (DF numerator)	14
F-TEST (DF denominator)	71
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	148.827411824352
Sum Squared Residuals	1572621.49423381

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	99.2	48.9067986613131	50.2932013386869
2	99	518.910628678044	-419.910628678044
3	631	647.652209749791	-16.6522097497911
4	-10.8	17.5534637179568	-28.3534637179568
5	-13	11.4346333920497	-24.4346333920497
6	833	544.959613875261	288.040386124739
7	586	646.46437079249	-60.4643707924905
8	-15.2	-8.7838053015356	-6.41619469846439
9	-4	13.0254264733486	-17.0254264733486
10	568	645.971969020659	-77.9719690206588
11	558	639.965637021501	-81.9656370215013
12	-14.9	12.6010099223793	-27.5010099223793
13	-9	20.7373653211106	-29.7373653211106
14	654	517.13339988164	136.866600118360
15	603	631.463765490598	-28.4637654905980
16	-7.8	14.4515853287716	-22.2515853287716
17	-3	9.71747021227763	-12.7174702122776
18	730	540.07658164019	189.923418359810
19	670	633.402740177847	36.5972598221529
20	-4.5	10.2004069066609	-14.7004069066609
21	0	7.80970077188205	-7.80970077188205
22	502	640.838978179472	-138.838978179472
23	625	638.143253619314	-13.1432536193139
24	-1.5	24.3216654720381	-25.8216654720381
25	0	17.7925038130849	-17.7925038130849
26	767	512.108558539047	254.891441460953
27	582	630.524806522489	-48.5248065224888
28	0.5	9.6882689099141	-9.1882689099141
29	4	5.12495582008065	-1.12495582008065
30	885	535.158606217584	349.841393782416
31	621	634.259040310137	-13.2590403101375
32	0.1	5.52293882630234	-5.42293882630234
33	-1	5.01043734302107	-6.01043734302107
34	994	636.060854203374	357.939145796626
35	696	633.314367774828	62.6856322251715
36	-3	19.3062108071049	-22.3062108071049
37	-1	10.5690580507082	-11.5690580507082
38	861	507.514694571019	353.485305428981
39	649	627.990681253062	21.0093187469385
40	-6.4	-0.430476991633618	-5.96952300836638
41	-6	0.294584172266221	-6.29458417226622
42	396	530.616723110341	-134.616723110341
43	613	621.769249640177	-8.76924964017737
44	-7.7	13.2495060547998	-20.9495060547998
45	-4	-0.642274943549959	-3.35772505645004
46	632	631.270633109826	0.729366890174338
47	634	631.014109975669	2.98589002433083
48	-2	-5.99194403543822	3.99194403543822
49	-3	7.68888542813224	-10.6888854281322
50	243	502.575766153134	-259.575766153134
51	706	624.120928724718	81.8790712752822
52	0.9	3.44124386601085	-2.54124386601085
53	1	-6.19385509318185	7.19385509318185
54	328	525.51861087048	-197.51861087048
55	644	626.813565379924	17.1864346200757
56	4.9	-16.8673752719116	21.7673752719116
57	1	-4.57359300201246	5.57359300201246
58	136	626.287360716587	-490.287360716587
59	612	621.336149828506	-9.3361498285065
60	6.5	-10.2260005825786	16.7260005825786
61	5	1.82284575103189	3.17715424896811
62	210	497.610205515554	-287.610205515554
63	618	612.287449952805	5.71255004719471
64	6.2	-18.416405934406	24.616405934406
65	8	-10.1670791422025	18.1670791422025
66	200	520.646600043039	-320.646600043039
67	668	624.249695823598	43.7503041764023
68	5.8	-11.4923890829588	17.2923890829588
69	4	-10.1073422511463	14.1073422511463
70	661	621.607246782647	39.3927532173529
71	644	618.015247892184	25.9847521078162
72	5.8	-26.6991789161331	32.4991789161331
73	6	-1.55440729359837	7.55440729359837
74	752	492.867297181985	259.132702818015
75	595	609.960158306538	-14.9601583065376
76	6.7	-36.9876788966139	43.6876788966139
77	4	-15.2107093612895	19.2107093612895
78	341	516.023264243106	-175.023264243106
79	594	609.041337875825	-15.041337875825
80	3	-5.42928213135723	8.42928213135723
81	0	-14.5223543915426	14.5223543915426
82	926	616.962957987435	309.037042012565
83	633	620.211233887997	12.7887661120031
84	1.8	-20.6117626673733	22.4117626673733
85	0	-8.7630497317822	8.7630497317822
86	451	488.279449479577	-37.2794494795773

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
18	0.85406398384107	0.291872032317859	0.145936016158929
19	0.746278002503608	0.507443994992785	0.253721997496392
20	0.721061863841407	0.557876272317187	0.278938136158593
21	0.60485672424127	0.790286551517461	0.395143275758730
22	0.524781826848492	0.950436346303017	0.475218173151508
23	0.417212859877514	0.834425719755028	0.582787140122486
24	0.327915359125649	0.655830718251299	0.67208464087435
25	0.242141918669929	0.484283837339858	0.757858081330071
26	0.423317421493182	0.846634842986364	0.576682578506818
27	0.364534581239193	0.729069162478386	0.635465418760807
28	0.287453661006988	0.574907322013976	0.712546338993012
29	0.219261955678854	0.438523911357708	0.780738044321146
30	0.324150196504559	0.648300393009118	0.675849803495441
31	0.257124516874216	0.514249033748431	0.742875483125784
32	0.207244587523491	0.414489175046981	0.79275541247651
33	0.155382998806766	0.310765997613531	0.844617001193234
34	0.508862847983264	0.982274304033471	0.491137152016736
35	0.436791958477967	0.873583916955934	0.563208041522033
36	0.373466270226825	0.746932540453651	0.626533729773175
37	0.316871900947850	0.633743801895699	0.68312809905215
38	0.65495594861316	0.69008810277368	0.34504405138684
39	0.589127046955413	0.821745906089174	0.410872953044587
40	0.524785116234473	0.950429767531055	0.475214883765527
41	0.467079767483551	0.934159534967102	0.532920232516449
42	0.766605139739393	0.466789720521213	0.233394860260607
43	0.720046481168286	0.559907037663429	0.279953518831714
44	0.668848814125186	0.662302371749627	0.331151185874813
45	0.603820859902955	0.79235828019409	0.396179140097045
46	0.564587843034418	0.870824313931165	0.435412156965582
47	0.49574856984557	0.99149713969114	0.50425143015443
48	0.427122074894699	0.854244149789397	0.572877925105301
49	0.366811888375817	0.733623776751634	0.633188111624183
50	0.487039646610504	0.974079293221008	0.512960353389496
51	0.43907936556071	0.87815873112142	0.56092063443929
52	0.368339593348529	0.736679186697057	0.631660406651471
53	0.301254629418458	0.602509258836917	0.698745370581542
54	0.384566935897776	0.769133871795552	0.615433064102224
55	0.321914972952691	0.643829945905383	0.678085027047309
56	0.262035847656393	0.524071695312786	0.737964152343607
57	0.210367003421862	0.420734006843723	0.789632996578138
58	0.879086268235415	0.241827463529170	0.120913731764585
59	0.826000278364275	0.347999443271451	0.173999721635725
60	0.764885623683363	0.470228752633275	0.235114376316637
61	0.692129139376724	0.615741721246552	0.307870860623276
62	0.92164438987986	0.156711220240281	0.0783556101201406
63	0.869917083293237	0.260165833413526	0.130082916706763
64	0.793658845978748	0.412682308042504	0.206341154021252
65	0.688986270404978	0.622027459190044	0.311013729595022
66	0.674040303769427	0.651919392461146	0.325959696230573
67	0.530744873564146	0.938510252871707	0.469255126435854
68	0.363583522867182	0.727167045734364	0.636416477132818

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	0	0	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/10ph1d1292965921.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/10ph1d1292965921.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/11gm11292965921.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/11gm11292965921.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/2b73m1292965921.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/2b73m1292965921.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/3b73m1292965921.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/3b73m1292965921.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/4b73m1292965921.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/4b73m1292965921.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/5b73m1292965921.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/5b73m1292965921.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/6mhl71292965921.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/6mhl71292965921.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/7xqka1292965921.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/7xqka1292965921.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/8xqka1292965921.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/8xqka1292965921.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/9ph1d1292965921.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292965800i8abhgoavucy65w/9ph1d1292965921.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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