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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 09:58:20 -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/t1258477155fira1iphq7e68qj.htm/, Retrieved Tue, 17 Nov 2009 17:59:27 +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/t1258477155fira1iphq7e68qj.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 «

19435,1 2,01 20604,6 20604,6 22686,8 2,01 19435,1 18714,9 20396,7 2,01 22686,8 19435,1 19233,6 2,01 20396,7 22686,8 22751 2,01 19233,6 20396,7 19864 2,01 22751 19233,6 17165,4 2,02 19864 22751 22309,7 2,02 17165,4 19864 21786,3 2,03 22309,7 17165,4 21927,6 2,05 21786,3 22309,7 20957,9 2,08 21927,6 21786,3 19726 2,07 20957,9 21927,6 21315,7 2,06 19726 20957,9 24771,5 2,05 21315,7 19726 22592,4 2,05 24771,5 21315,7 21942,1 2,05 22592,4 24771,5 23973,7 2,05 21942,1 22592,4 20815,7 2,05 23973,7 21942,1 19931,4 2,06 20815,7 23973,7 24436,8 2,06 19931,4 20815,7 22838,7 2,07 24436,8 19931,4 24465,3 2,07 22838,7 24436,8 23007,3 2,3 24465,3 22838,7 22720,8 2,31 23007,3 24465,3 23045,7 2,31 22720,8 23007,3 27198,5 2,53 23045,7 22720,8 22401,9 2,58 27198,5 23045,7 25122,7 2,59 22401,9 27198,5 26100,5 2,73 25122,7 22401,9 22904,9 2,82 26100,5 25122,7 22040,4 3 22904,9 26100,5 25981,5 3,04 22040,4 22904,9 26157,1 3,23 25981,5 22040,4 25975,4 3,32 26157,1 25981,5 22589,8 3,49 25975,4 26157 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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 12447.5734764417 + 1520.23422378143X[t] + 0.106331105171884Y1[t] + 0.125660111430486Y2[t] + 574.20093296977M1[t] + 3263.51004141076M2[t] + 589.203841116118M3[t] + 382.499042157921M4[t] + 3052.49597765317M5[t] + 539.841441463288M6[t] -1942.11286983767M7[t] + 2766.28626388718M8[t] + 2060.45288283293M9[t] + 1169.12828136410M10[t] -1273.00396028459M11[t] + 19.7034479645541t + 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)	12447.5734764417	3567.001058	3.4896	0.001006	0.000503
X	1520.23422378143	445.671041	3.4111	0.001274	0.000637
Y1	0.106331105171884	0.155591	0.6834	0.497446	0.248723
Y2	0.125660111430486	0.148715	0.845	0.402073	0.201036
M1	574.20093296977	880.305027	0.6523	0.517153	0.258577
M2	3263.51004141076	869.628298	3.7528	0.000449	0.000224
M3	589.203841116118	1034.254264	0.5697	0.571389	0.285695
M4	382.499042157921	859.645037	0.4449	0.658239	0.329119
M5	3052.49597765317	841.848024	3.6259	0.000665	0.000332
M6	539.841441463288	1009.823017	0.5346	0.595256	0.297628
M7	-1942.11286983767	837.00927	-2.3203	0.024362	0.012181
M8	2766.28626388718	883.165398	3.1322	0.002872	0.001436
M9	2060.45288283293	1143.241283	1.8023	0.077411	0.038705
M10	1169.12828136410	980.118941	1.1928	0.238451	0.119225
M11	-1273.00396028459	920.747315	-1.3826	0.172819	0.08641
t	19.7034479645541	11.731123	1.6796	0.099154	0.049577

Multiple Linear Regression - Regression Statistics
Multiple R	0.923312363581884
R-squared	0.852505720743166
Adjusted R-squared	0.809125050373508
F-TEST (value)	19.6517415124930
F-TEST (DF numerator)	15
F-TEST (DF denominator)	51
p-value	4.44089209850063e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1310.01717342384
Sum Squared Residuals	87523394.7279346

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	19435.1	20877.2348687819	-1442.1348687819
2	22686.8	23224.4332851187	-537.633285118731
3	20396.7	21006.0877997283	-609.38779972829
4	19233.6	20984.1865691190	-1750.58656911903
5	22751	23262.4390229665	-511.439022966454
6	19864	20997.3416884679	-1133.34168846792
7	17165.4	18685.3121426837	-1519.91214268369
8	22309.7	22763.6888622564	-453.988862256438
9	21786.3	22300.6539990340	-514.353999033964
10	21927.6	22050.2171407902	-122.617140790207
11	20957.9	19622.6494566576	1335.25054334242
12	19726	20814.8010237289	-1088.80102372886
13	21315.7	21140.66116391	175.038836090014
14	24771.5	23848.7052446982	922.794755301759
15	22592.4	21761.3234047622	831.076595237806
16	21942.1	21776.8721555700	165.227844430024
17	23973.7	24123.5994725183	-149.899472518324
18	20815.7	21764.9538870970	-949.253887096954
19	19931.4	19237.4028182477	693.997181752267
20	24436.8	23474.6421717362	962.15782826383
21	22838.7	23171.6575055877	-332.957505587709
22	24465.3	22696.2576789472	1769.04232105284
23	23007.3	20595.6235083283	2411.67649167172
24	22720.8	21952.9012447275	767.898755272541
25	23045.7	22333.1293215644	712.570678435611
26	27198.5	25375.1387613474	1823.36123865265
27	22401.9	23278.9465039679	-877.046503967903
28	25122.7	23118.9610268931	2003.73897310686
29	26100.5	25708.0585821465	392.441417853467
30	22904.9	23797.7951598787	-892.89515987867
31	22040.4	21392.2652340924	648.134765907616
32	25981.5	25687.6944922247	293.805507775306
33	26157.1	25600.8374139147	556.262586085276
34	25975.4	25379.9481477776	595.451852222354
35	22589.8	23218.7047258938	-628.904725893817
36	25370.4	24250.2038401286	1120.19615987138
37	25091.1	24699.135276607	391.964723392978
38	28760.9	27971.0975369867	789.802463013308
39	24325.9	25823.635227672	-1497.73522767199
40	25821.7	25626.2029021686	195.497097831355
41	27645.7	28160.8882343554	-515.188234355371
42	26296.9	26171.4662145438	125.433785456204
43	24141.5	23825.4046842764	316.095315723605
44	27268.1	28322.0566081968	-1053.95660819684
45	29060.3	27651.9266776468	1408.37332235316
46	28226.4	27378.963377468	847.436622532013
47	23268.5	25153.882495838	-1885.38249583799
48	26938.2	25814.6229508336	1123.57704916643
49	27217.5	26114.9109530047	1102.58904699531
50	27540.5	29345.1613824768	-1804.66138247682
51	29167.6	26851.2144996667	2316.38550033332
52	26671.5	26877.8127058903	-206.312705890253
53	30184	29430.54987385	753.450126150013
54	28422.3	27195.0570374909	1227.24296250915
55	23774.3	25108.4825454752	-1334.18254547524
56	29601	29349.0178655859	251.982134414143
57	28523.6	29640.9244038168	-1117.32440381676
58	23622	26711.313655017	-3089.313655017
59	21320.3	22552.9398132823	-1232.63981328233
60	20423.6	22346.4709405815	-1922.87094058148
61	21174.9	22114.928416132	-940.02841613201
62	23050.2	24243.8637893722	-1193.66378937216
63	21202.9	21366.1925642029	-163.292564202941
64	20476.4	20883.9646403590	-407.564640358954
65	23173.3	23142.6648141633	30.6351858366688
66	22468	20845.1860125218	1622.81398747819
67	19842.7	18646.8325752246	1195.86742477545

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.0846041860192285	0.169208372038457	0.915395813980772
20	0.0307778019930026	0.0615556039860053	0.969222198006997
21	0.0103109237241959	0.0206218474483918	0.989689076275804
22	0.00426560703558421	0.00853121407116842	0.995734392964416
23	0.00575990204776093	0.0115198040955219	0.99424009795224
24	0.00576482829417157	0.0115296565883431	0.994235171705828
25	0.00365661746831782	0.00731323493663564	0.996343382531682
26	0.00183131124258231	0.00366262248516462	0.998168688757418
27	0.0133501745301296	0.0267003490602591	0.98664982546987
28	0.0168706877423915	0.0337413754847831	0.983129312257608
29	0.0095546003157616	0.0191092006315232	0.990445399684238
30	0.00805332107016252	0.0161066421403250	0.991946678929837
31	0.00442477536946233	0.00884955073892467	0.995575224630538
32	0.00206402940962663	0.00412805881925326	0.997935970590373
33	0.00108998077315827	0.00217996154631655	0.998910019226842
34	0.000474349047266234	0.000948698094532468	0.999525650952734
35	0.00302010206902078	0.00604020413804155	0.99697989793098
36	0.0017985481030325	0.003597096206065	0.998201451896968
37	0.000809896067365034	0.00161979213473007	0.999190103932635
38	0.000679830442863812	0.00135966088572762	0.999320169557136
39	0.00122571095919822	0.00245142191839644	0.998774289040802
40	0.000565146274721745	0.00113029254944349	0.999434853725278
41	0.000416479599066389	0.000832959198132778	0.999583520400934
42	0.00197369306972074	0.00394738613944149	0.99802630693028
43	0.00604783128049471	0.0120956625609894	0.993952168719505
44	0.0252664069336512	0.0505328138673025	0.974733593066349
45	0.0227075486005971	0.0454150972011941	0.977292451399403
46	0.0118362353480434	0.0236724706960869	0.988163764651957
47	0.126745079050595	0.253490158101191	0.873254920949405
48	0.0702169230969474	0.140433846193895	0.929783076903053

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	15	0.5	NOK
5% type I error level	25	0.833333333333333	NOK
10% type I error level	27	0.9	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/10hg4f1258477094.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/10hg4f1258477094.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/1n5q51258477094.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/1n5q51258477094.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/2npd71258477094.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/2npd71258477094.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/3l0li1258477094.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/3l0li1258477094.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/45igl1258477094.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/45igl1258477094.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/56gou1258477094.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/56gou1258477094.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/6b7b71258477094.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/6b7b71258477094.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/7hany1258477094.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/7hany1258477094.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/8km9i1258477094.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/8km9i1258477094.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/9c8df1258477094.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258477155fira1iphq7e68qj/9c8df1258477094.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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