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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: Thu, 19 Nov 2009 02:51:18 -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/19/t125862432447ct35t9xd03qs9.htm/, Retrieved Thu, 19 Nov 2009 10:52:17 +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/19/t125862432447ct35t9xd03qs9.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 «

519 97.4 517 97 510 105.4 509 102.7 501 98.1 507 104.5 569 87.4 580 89.9 578 109.8 565 111.7 547 98.6 555 96.9 562 95.1 561 97 555 112.7 544 102.9 537 97.4 543 111.4 594 87.4 611 96.8 613 114.1 611 110.3 594 103.9 595 101.6 591 94.6 589 95.9 584 104.7 573 102.8 567 98.1 569 113.9 621 80.9 629 95.7 628 113.2 612 105.9 595 108.8 597 102.3 593 99 590 100.7 580 115.5 574 100.7 573 109.9 573 114.6 620 85.4 626 100.5 620 114.8 588 116.5 566 112.9 557 102 561 106 549 105.3 532 118.8 526 106.1 511 109.3 499 117.2 555 92.5 565 104.2 542 112.5 527 122.4 510 113.3 514 100 517 110.7 508 112.8 493 109.8 490 117.3 469 109.1 478 115.9 528 96 534 99.8 518 116.8 506 115.7 502 99.4 516 94.3

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] = + 611.409334928083 -0.270247144144274X[t] -5.79884393448892M1[t] -9.67956344989054M2[t] -16.3712953588341M3[t] -23.567174859071M4[t] -33.0244073542023M5[t] -27.9999130256084M6[t] + 19.0253656640922M7[t] + 31.9597633501936M8[t] + 29.2273517585181M9[t] + 14.9727760992730M10[t] -2.22756473669992M11[t] -0.686870792856947t + 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)	611.409334928083	102.132788	5.9864	0	0
X	-0.270247144144274	1.082846	-0.2496	0.803801	0.4019
M1	-5.79884393448892	20.755812	-0.2794	0.780944	0.390472
M2	-9.67956344989054	20.877896	-0.4636	0.64465	0.322325
M3	-16.3712953588341	24.895068	-0.6576	0.513389	0.256694
M4	-23.567174859071	21.906802	-1.0758	0.286475	0.143237
M5	-33.0244073542023	21.247907	-1.5542	0.125567	0.062783
M6	-27.9999130256084	25.684772	-1.0901	0.280161	0.140081
M7	19.0253656640922	23.395589	0.8132	0.419427	0.209714
M8	31.9597633501936	20.484405	1.5602	0.124153	0.062077
M9	29.2273517585181	25.763968	1.1344	0.261282	0.130641
M10	14.9727760992730	25.801105	0.5803	0.56395	0.281975
M11	-2.22756473669992	21.717199	-0.1026	0.918657	0.459328
t	-0.686870792856947	0.263334	-2.6084	0.011551	0.005775

Multiple Linear Regression - Regression Statistics
Multiple R	0.62323044184832
R-squared	0.388416183646452
Adjusted R-squared	0.251337052394795
F-TEST (value)	2.83351798410056
F-TEST (DF numerator)	13
F-TEST (DF denominator)	58
p-value	0.00330494121873914
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	35.3937833758541
Sum Squared Residuals	72657.7542960995

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	519	578.601548361082	-59.6015483610821
2	517	574.142056910484	-57.1420569104838
3	510	564.493378197871	-54.4933781978713
4	509	557.340295193967	-48.340295193967
5	501	548.439328769043	-47.4393287690425
6	507	551.047370582256	-44.0473705822561
7	569	602.007004643967	-33.0070046439668
8	580	613.57891367685	-33.5789136768505
9	578	604.781713123847	-26.7817131238471
10	565	589.326797097871	-24.3267970978708
11	547	574.979823057331	-27.979823057331
12	555	576.979937146219	-21.9799371462193
13	562	570.980667278333	-8.9806672783331
14	561	565.8996073962	-4.89960739620039
15	555	554.278124531335	0.721875468665248
16	544	549.043796250855	-5.0437962508548
17	537	540.38605225566	-3.38605225566009
18	543	540.940215773377	2.05978422662282
19	594	593.764555129683	0.235444870316577
20	611	603.471758867972	7.52824113202827
21	613	595.377200889743	17.6227991102567
22	611	581.462693585389	29.5373064146106
23	594	565.305063679083	28.694936320917
24	595	567.467326054458	27.5326739455422
25	591	562.873341336122	28.1266586638781
26	589	557.954429740476	31.0455702595243
27	584	548.197652170206	35.8023478297944
28	573	540.828371450986	32.1716285490141
29	567	531.954429740476	35.0455702595243
30	569	532.022148398733	36.9778516012669
31	621	587.278712052338	33.7212879476622
32	629	595.526581212247	33.4734187877529
33	628	587.37797380519	40.6220261948102
34	612	574.409331505341	37.5906684946591
35	595	555.738403158493	39.2615968415073
36	597	559.035703539273	37.9642964607266
37	593	553.441804387604	39.5581956123963
38	590	548.4147939343	41.5852060657001
39	580	537.036533499164	42.9634665008359
40	574	533.153440939406	40.8465590605945
41	573	520.52306392529	52.4769360747101
42	573	523.590525883549	49.4094741164512
43	620	577.820150389405	42.1798496105948
44	626	585.986945406071	40.0130545939288
45	620	578.703128860276	41.2968711397244
46	588	563.302262263128	24.6977377368718
47	566	546.387940353218	19.6120596467822
48	557	550.874328168233	6.12567183176662
49	561	543.30762486431	17.6923751356896
50	549	538.929207556953	10.0707924430472
51	532	527.902268409205	4.09773159079539
52	526	523.451656846743	2.54834315325695
53	511	512.442762697493	-1.44276269749314
54	499	514.64543379449	-15.6454337944903
55	555	567.658946151698	-12.6589461516976
56	565	576.744581458454	-11.744581458454
57	542	571.082247777524	-29.0822477775240
58	527	553.465354598394	-26.4653545983937
59	510	538.037391981277	-28.0373919812767
60	514	543.172372942239	-29.1723729422386
61	517	533.795013772549	-16.7950137725489
62	508	528.659904461587	-20.6599044615874
63	493	522.09204319222	-29.0920431922197
64	490	512.182439318044	-22.1824393180438
65	469	504.254362612039	-35.2543626120386
66	478	506.754305567595	-28.7543055675945
67	528	558.470631632909	-30.4706316329092
68	534	569.691219378405	-35.6912193784055
69	518	561.67773554342	-43.6777355434203
70	506	547.033560949877	-41.0335609498770
71	502	533.551377770599	-31.5513777705988
72	516	536.470332149578	-20.4703321495776

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.00644778127808402	0.0128955625561680	0.993552218721916
18	0.00225279661393863	0.00450559322787726	0.997747203386061
19	0.0054425898603343	0.0108851797206686	0.994557410139666
20	0.00322354028482769	0.00644708056965537	0.996776459715172
21	0.00123466300703667	0.00246932601407334	0.998765336992963
22	0.000743526827240503	0.00148705365448101	0.99925647317276
23	0.000598897642356057	0.00119779528471211	0.999401102357644
24	0.000274135801131366	0.000548271602262733	0.999725864198869
25	0.000204154207093493	0.000408308414186985	0.999795845792907
26	0.000129175832117017	0.000258351664234033	0.999870824167883
27	5.47284121447155e-05	0.000109456824289431	0.999945271587855
28	5.94250176119117e-05	0.000118850035223823	0.999940574982388
29	4.82197329504265e-05	9.64394659008531e-05	0.99995178026705
30	8.12224824216973e-05	0.000162444964843395	0.999918777517578
31	0.000154868672867804	0.000309737345735607	0.999845131327132
32	0.00147085936930335	0.00294171873860670	0.998529140630697
33	0.00346453978311962	0.00692907956623924	0.99653546021688
34	0.0126404112874278	0.0252808225748555	0.987359588712572
35	0.0412199339851324	0.0824398679702648	0.958780066014868
36	0.0715712124309706	0.143142424861941	0.92842878756903
37	0.121654221938479	0.243308443876959	0.87834577806152
38	0.150450693562218	0.300901387124436	0.849549306437782
39	0.167958104097917	0.335916208195835	0.832041895902083
40	0.189403232147621	0.378806464295242	0.810596767852379
41	0.207690248243707	0.415380496487415	0.792309751756293
42	0.253628827267871	0.507257654535741	0.746371172732129
43	0.274459732322652	0.548919464645304	0.725540267677348
44	0.353350784173529	0.706701568347057	0.646649215826471
45	0.782750234149327	0.434499531701347	0.217249765850673
46	0.932521835768292	0.134956328463416	0.0674781642317078
47	0.981572207588882	0.0368555848222359	0.0184277924111179
48	0.989122348504822	0.0217553029903568	0.0108776514951784
49	0.987857342742398	0.0242853145152030	0.0121426572576015
50	0.98448859363279	0.0310228127344214	0.0155114063672107
51	0.993144810698055	0.0137103786038904	0.00685518930194521
52	0.987270368096989	0.0254592638060227	0.0127296319030113
53	0.99063247342898	0.0187350531420395	0.00936752657101974
54	0.97717447274714	0.0456510545057187	0.0228255272528593
55	0.93686765234524	0.126264695309522	0.0631323476547608

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	15	0.384615384615385	NOK
5% type I error level	26	0.666666666666667	NOK
10% type I error level	27	0.692307692307692	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/10cbm51258624273.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/10cbm51258624273.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/11f9e1258624273.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/11f9e1258624273.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/2kd3z1258624273.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/2kd3z1258624273.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/3wwu11258624273.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/3wwu11258624273.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/4idn71258624273.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/4idn71258624273.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/5pkv31258624273.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/5pkv31258624273.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/6d1b21258624273.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/6d1b21258624273.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/7lcy91258624273.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/7lcy91258624273.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/8qbl31258624273.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/8qbl31258624273.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/95d4q1258624273.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t125862432447ct35t9xd03qs9/95d4q1258624273.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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