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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: Sun, 05 Dec 2010 21:30:44 +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/05/t1291584605kfxyc501sssbvd6.htm/, Retrieved Sun, 05 Dec 2010 22:30:15 +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/05/t1291584605kfxyc501sssbvd6.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 «

2293,41 10430,35 9374,63 21467 -18,2 -11 -0,8 3,52 2443,27 2513,17 2466,92 2502,66 2070,83 9691,12 8679,75 21383 -22,8 -17 -1,7 3,54 2293,41 2443,27 2513,17 2466,92 2029,6 9810,31 8593 21777 -23,6 -18 -1,1 3,5 2070,83 2293,41 2443,27 2513,17 2052,02 9304,43 8398,37 21928 -27,6 -19 -0,4 3,44 2029,6 2070,83 2293,41 2443,27 1864,44 8767,96 7992,12 21814 -29,4 -22 0,6 3,38 2052,02 2029,6 2070,83 2293,41 1670,07 7764,58 7235,47 22937 -31,8 -24 0,6 3,35 1864,44 2052,02 2029,6 2070,83 1810,99 7694,78 7690,5 23595 -31,4 -24 1,9 3,68 1670,07 1864,44 2052,02 2029,6 1905,41 8331,49 8396,2 20830 -27,6 -20 2,3 3,92 1810,99 1670,07 1864,44 2052,02 1862,83 8460,94 8595,56 19650 -28,8 -25 2,6 4,05 1905,41 1810,99 1670,07 1864,44 2014,45 8531,45 8614,55 19195 -21,9 -22 3,1 4,14 1862,83 1905,41 1810,99 1670,07 2197,82 9117,03 9181,73 19644 -13,9 -17 4,7 4,53 2014,45 1862,83 1905,41 1810,99 2962,34 12123,53 11114,08 18483 -8 -9 5,5 4,54 2197,82 2014,45 1862,83 1905,41 3047,03 12989,35 11530,75 18079 -2,8 -11 5,4 4,9 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

BEL_20[t] = -437.150130487514 + 0.0822460593228443Nikkei[t] + 0.157632205011041DJ_Indust[t] -0.0356323334275397Goudprijs[t] -4.97051484989923Conjunct_Seizoenzuiver[t] -0.505827584503886Cons_vertrouw[t] + 55.8217095027264Alg_consumptie_index_BE[t] -32.8367316002704Gem_rente_kasbon_5j[t] + 0.273525857881991Y1[t] -0.0033510421496098Y2[t] + 0.118967771142002Y3[t] + 0.141804980701454Y4[t] -53.7213924732792M1[t] -40.1662586828167M2[t] -26.8055842768575M3[t] + 53.3258062955028M4[t] + 59.9822935133613M5[t] + 49.9516938387192M6[t] + 76.3109196672503M7[t] -6.06146549961345M8[t] -77.0522153153102M9[t] -18.0618843132753M10[t] + 17.6333117110356M11[t] -9.75190391682969t + 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)	-437.150130487514	557.507462	-0.7841	0.437174	0.218587
Nikkei	0.0822460593228443	0.012405	6.6303	0	0
DJ_Indust	0.157632205011041	0.024363	6.4701	0	0
Goudprijs	-0.0356323334275397	0.015009	-2.374	0.022026	0.011013
Conjunct_Seizoenzuiver	-4.97051484989923	5.318399	-0.9346	0.355101	0.177551
Cons_vertrouw	-0.505827584503886	5.08118	-0.0995	0.921154	0.460577
Alg_consumptie_index_BE	55.8217095027264	11.957079	4.6685	2.9e-05	1.4e-05
Gem_rente_kasbon_5j	-32.8367316002704	44.625418	-0.7358	0.465739	0.232869
Y1	0.273525857881991	0.105941	2.5819	0.013233	0.006617
Y2	-0.0033510421496098	0.109178	-0.0307	0.975653	0.487826
Y3	0.118967771142002	0.108408	1.0974	0.278432	0.139216
Y4	0.141804980701454	0.078859	1.7982	0.079008	0.039504
M1	-53.7213924732792	50.379829	-1.0663	0.292092	0.146046
M2	-40.1662586828167	51.427424	-0.781	0.438968	0.219484
M3	-26.8055842768575	50.167432	-0.5343	0.595808	0.297904
M4	53.3258062955028	51.579688	1.0339	0.306854	0.153427
M5	59.9822935133613	56.411156	1.0633	0.293444	0.146722
M6	49.9516938387192	59.75632	0.8359	0.407715	0.203857
M7	76.3109196672503	61.056007	1.2499	0.217961	0.108981
M8	-6.06146549961345	55.750862	-0.1087	0.913916	0.456958
M9	-77.0522153153102	54.904333	-1.4034	0.167519	0.083759
M10	-18.0618843132753	53.197752	-0.3395	0.735831	0.367915
M11	17.6333117110356	52.060199	0.3387	0.736439	0.36822
t	-9.75190391682969	3.70872	-2.6295	0.011738	0.005869

Multiple Linear Regression - Regression Statistics
Multiple R	0.99701564320794
R-squared	0.99404019280134
Adjusted R-squared	0.990924839038406
F-TEST (value)	319.077789696899
F-TEST (DF numerator)	23
F-TEST (DF denominator)	44
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	81.0167551044207
Sum Squared Residuals	288803.442736586

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	2293.41	2314.09027639276	-20.6802763927601
2	2070.83	2085.23326404422	-14.4032640442217
3	2029.6	2048.08337850274	-18.4833785027381
4	2052.02	2063.95680329801	-11.93680329801
5	1864.44	1983.55904105403	-119.119041054026
6	1670.07	1648.03985626003	22.0301437399714
7	1810.99	1711.21561063193	99.7743893680729
8	1905.41	1894.81915954437	10.5908404556259
9	1862.83	1894.79700019651	-31.9670001965138
10	2014.45	1935.42142567879	79.0285743212066
11	2197.82	2189.97920747076	7.84079252923915
12	2962.34	2824.76477178693	137.575228213071
13	3047.03	3090.84076132081	-43.8107613208125
14	3032.6	3132.07592685282	-99.4759268528232
15	3504.37	3466.41063947719	37.9593605228114
16	3801.06	3868.33603465777	-67.2760346577655
17	3857.62	3836.63330600387	20.9866939961319
18	3674.4	3683.21063167134	-8.8106316713421
19	3720.98	3809.57240307486	-88.5924030748574
20	3844.49	3829.93495684367	14.5550431563330
21	4116.68	4101.43541855698	15.2445814430163
22	4105.18	4152.54896983023	-47.3689698302346
23	4435.23	4395.93709879218	39.2929012078197
24	4296.49	4370.98878675174	-74.4987867517418
25	4202.52	4275.23017188377	-72.7101718837678
26	4562.84	4490.78660709373	72.053392906266
27	4621.4	4583.9328213805	37.4671786194967
28	4696.96	4605.55072738551	91.4092726144865
29	4591.27	4560.71301511583	30.5569848841681
30	4356.98	4485.4887378022	-128.508737802202
31	4502.64	4541.83228975478	-39.1922897547817
32	4443.91	4458.58791416613	-14.6779141661265
33	4290.89	4263.40487479395	27.4851252060536
34	4199.75	4153.25287704426	46.4971229557413
35	4138.52	4168.38511977971	-29.8651197797128
36	3970.1	3984.96386458212	-14.8638645821219
37	3862.27	3776.81578443947	85.454215560534
38	3701.61	3631.65691836561	69.9530816343925
39	3570.12	3602.64067283117	-32.5206728311659
40	3801.06	3724.12533691505	76.9346630849504
41	3895.51	3835.74763512091	59.7623648790891
42	3917.96	3754.89279280640	163.067207193605
43	3813.06	3792.51070697236	20.5492930276419
44	3667.03	3721.84149336537	-54.8114933653671
45	3494.17	3622.83850288249	-128.668502882494
46	3363.99	3511.43696877591	-147.446968775907
47	3295.32	3351.18397292840	-55.8639729283959
48	3277.01	3337.38421243919	-60.3742124391898
49	3257.16	3197.34548997678	59.8145100232234
50	3161.69	3137.46089563491	24.2291043650907
51	3097.31	3050.16374039400	47.1462596059962
52	3061.26	3054.43318788269	6.82681211730801
53	3119.31	3047.72306145014	71.5869385498575
54	3106.22	3124.00432994056	-17.7843299405627
55	3080.58	3080.90717831594	-0.327178315941167
56	2981.85	2914.38942102375	67.4605789762528
57	2921.44	2803.53420357006	117.905796429938
58	2849.27	2779.97975867081	69.2902413291934
59	2756.76	2718.16460102895	38.59539897105
60	2645.64	2633.47836444002	12.1616355599831
61	2497.84	2505.90751598642	-8.06751598641694
62	2448.05	2500.40638800870	-52.3563880087043
63	2454.62	2526.1887474144	-71.5687474144003
64	2407.6	2503.55790986097	-95.9579098609695
65	2472.81	2536.58394125522	-63.7739412552211
66	2408.64	2438.63365151947	-29.9936515194696
67	2440.25	2432.46181125013	7.78818874986562
68	2350.44	2373.55705505672	-23.1170550567181

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
27	0.579031705810466	0.841936588379067	0.420968294189534
28	0.640851157887756	0.718297684224487	0.359148842112243
29	0.547541079611132	0.904917840777737	0.452458920388868
30	0.786752471714185	0.426495056571629	0.213247528285815
31	0.788720322638737	0.422559354722525	0.211279677361263
32	0.898398722019614	0.203202555960773	0.101601277980386
33	0.829363501748993	0.341272996502013	0.170636498251007
34	0.941147504321899	0.117704991356203	0.0588524956781013
35	0.92013078446536	0.159738431069281	0.0798692155346404
36	0.863024121448724	0.273951757102552	0.136975878551276
37	0.771430370508041	0.457139258983918	0.228569629491959
38	0.727168768219835	0.545662463560329	0.272831231780165
39	0.999765463589532	0.000469072820935074	0.000234536410467537
40	0.999179773345739	0.00164045330852254	0.000820226654261268
41	0.995038238006583	0.00992352398683306	0.00496176199341653

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.2	NOK
5% type I error level	3	0.2	NOK
10% type I error level	3	0.2	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/10jx701291584636.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/10jx701291584636.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/1ceao1291584636.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/1ceao1291584636.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/255991291584636.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/255991291584636.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/355991291584636.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/355991291584636.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/455991291584636.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/455991291584636.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/5fw8c1291584636.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/5fw8c1291584636.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/6fw8c1291584636.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/6fw8c1291584636.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/78nqf1291584636.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/78nqf1291584636.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/88nqf1291584636.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/88nqf1291584636.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/9jx701291584636.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291584605kfxyc501sssbvd6/9jx701291584636.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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