paper multiple regressie 2 vertragingen

*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: Mon, 28 Dec 2009 08:30:17 -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/Dec/28/t12620142646gjbxzmngmeaqks.htm/, Retrieved Mon, 28 Dec 2009 16:31:16 +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/Dec/28/t12620142646gjbxzmngmeaqks.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:

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User-defined keywords:

Dataseries X:

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91.6 0 104.6 111.6 98.3 0 91.6 104.6 97.7 0 98.3 91.6 106.3 0 97.7 98.3 102.3 0 106.3 97.7 106.6 0 102.3 106.3 108.1 0 106.6 102.3 93.8 0 108.1 106.6 88.2 0 93.8 108.1 108.9 0 88.2 93.8 114.2 0 108.9 88.2 102.5 0 114.2 108.9 94.2 0 102.5 114.2 97.4 0 94.2 102.5 98.5 0 97.4 94.2 106.5 0 98.5 97.4 102.9 0 106.5 98.5 97.1 0 102.9 106.5 103.7 0 97.1 102.9 93.4 0 103.7 97.1 85.8 0 93.4 103.7 108.6 0 85.8 93.4 110.2 0 108.6 85.8 101.2 0 110.2 108.6 101.2 0 101.2 110.2 96.9 0 101.2 101.2 99.4 0 96.9 101.2 118.7 0 99.4 96.9 108.0 0 118.7 99.4 101.2 0 108.0 118.7 119.9 0 101.2 108.0 94.8 0 119.9 101.2 95.3 0 94.8 119.9 118.0 0 95.3 94.8 115.9 0 118.0 95.3 111.4 0 115.9 118.0 108.2 0 111.4 115.9 108.8 0 108.2 111.4 109.5 0 108.8 108.2 124.8 0 109.5 108.8 115.3 0 124.8 109.5 109.5 0 115.3 124.8 124.2 0 109.5 115.3 92.9 0 124.2 109.5 98.4 0 92.9 124.2 120.9 0 98.4 92.9 111.7 0 120.9 98.4 116.1 0 111.7 120.9 109.4 0 116.1 111.7 111.7 0 109.4 116.1 114.3 0 111.7 109.4 133. 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation y[t] = + 36.8472879000015 -11.7370202770488dummy[t] + 0.181702401154939y1[t] + 0.398199895750638y2[t] -6.57944589025193M1[t] -0.385809419381148M2[t] + 2.9364369009721M3[t] + 14.0523966076367M4[t] + 4.18719139691463M5[t] -1.04841000566624M6[t] + 11.8171055119117M7[t] -9.72042959471114M8[t] -12.2101762942471M9[t] + 18.7224391046008M10[t] + 16.9993335335749M11[t] + 0.186416117482880t + 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) 36.8472879000015 10.459827 3.5227 0.000717 0.000359 dummy -11.7370202770488 2.621675 -4.4769 2.6e-05 1.3e-05 y1 0.181702401154939 0.100819 1.8023 0.075368 0.037684 y2 0.398199895750638 0.09382 4.2443 6e-05 3e-05 M1 -6.57944589025193 2.638941 -2.4932 0.014777 0.007388 M2 -0.385809419381148 2.850528 -0.1353 0.892686 0.446343 M3 2.9364369009721 2.861484 1.0262 0.307972 0.153986 M4 14.0523966076367 2.824765 4.9747 4e-06 2e-06 M5 4.18719139691463 2.720531 1.5391 0.127825 0.063913 M6 -1.04841000566624 2.662367 -0.3938 0.694811 0.347406 M7 11.8171055119117 2.702697 4.3723 3.8e-05 1.9e-05 M8 -9.72042959471114 2.643815 -3.6767 0.000432 0.000216 M9 -12.2101762942471 3.363922 -3.6297 0.000505 0.000253 M10 18.7224391046008 3.260348 5.7425 0 0 M11 16.9993335335749 3.169411 5.3636 1e-06 0 t 0.186416117482880 0.046508 4.0082 0.000139 6.9e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.943104661617373 R-squared 0.88944640276442 Adjusted R-squared 0.868186095603732 F-TEST (value) 41.8360090492513 F-TEST (DF numerator) 15 F-TEST (DF denominator) 78 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.79920393781965 Sum Squared Residuals 1796.52395806913 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 91.6 93.8994376538101 -2.29943765381014 2 98.3 95.1299597568952 3.1700402431048 3 97.7 94.6794296377111 3.02057036228890 4 106.3 108.540723322695 -2.24072332269497 5 102.3 100.185654941938 2.11434505806219 6 106.6 97.8341791556755 8.76582084432444 7 108.1 110.0746315327 -1.97463153270003 8 93.8 90.7083256970202 3.09167430297979 9 88.2 86.4039506220775 1.7960493779225 10 108.9 110.811190182706 -1.91119018270649 11 114.2 110.805821016867 3.39417898313289 12 102.5 103.198664168935 -0.69866416893451 13 94.2 96.790175750131 -2.59017575013104 14 97.4 97.0031596286162 0.396840371383766 15 98.5 97.788210615418 0.711789384582096 16 106.5 110.564698747238 -4.06469874723791 17 102.9 102.777548748564 0.122451251436144 18 97.1 100.259833985313 -3.15983398531319 19 103.7 110.824372068973 -7.12437206897303 20 93.4 88.362929532102 5.037070467898 21 85.8 86.8161835301073 -1.01618353010729 22 108.6 112.452817871429 -3.85281787142894 23 110.2 112.032623956514 -1.83262395651365 24 101.2 104.589388005384 -3.3893880053841 25 101.2 97.1981564554216 4.00184354457838 26 96.9 99.9944099820195 -3.09440998201954 27 99.4 102.721752094889 -3.32175209488944 28 118.7 112.766124370197 5.93387562980342 29 108 107.589691358624 0.410308641375751 30 101.2 108.281548369156 -7.0815483691557 31 119.9 115.837164791831 4.06283520816892 32 94.8 95.1761214131842 -0.376121413184189 33 95.3 95.758398612679 -0.458398612679079 34 118 116.973463946246 1.02653605375370 35 115.9 119.760518946796 -3.86051894679569 36 111.4 111.605164121818 -0.205164121817798 37 108.2 103.558253762775 4.64174623722481 38 108.8 107.564959136555 1.23504086344482 39 109.5 109.908403348682 -0.40840334868223 40 124.8 121.576890791089 3.22310920891139 41 115.3 114.956888362545 0.343111637454628 42 109.5 114.273988671460 -4.7739886714602 43 124.2 122.489147370191 1.71085262980871 44 92.9 101.499494282675 -8.59949428267527 45 98.4 99.362417012007 -0.962417012006983 46 120.9 119.017154997695 1.88284500230505 47 111.7 123.758868996767 -12.0588689967666 48 116.1 114.233787144438 1.86621285556153 49 109.4 104.976808895845 4.42319110415472 50 111.7 111.891534937764 -0.191534937763654 51 114.3 113.150173596727 1.14982640327312 52 133.7 125.840835424104 7.85916457589627 53 114.3 120.722392642222 -6.42239264222195 54 126.5 119.873258752280 6.62674124771951 55 131 127.416881703869 3.58311829613082 56 104 111.741462248084 -7.74146224808426 57 108.9 106.324066365726 2.5759336342743 58 128.5 127.582042462448 0.917957537551546 59 132.4 131.557899560720 0.842100439279676 60 128 123.258339465845 4.7416605341549 61 116.4 117.618798721422 -1.21879872142180 62 120.9 120.139023915075 0.760976084924634 63 118.6 119.846228367401 -1.24622836740134 64 133.1 132.522588199770 0.577411800229614 65 121.1 124.562624163051 -3.46262416305128 66 127.6 123.106908552478 4.49309144752174 67 135.4 132.561507046038 2.83849295396151 68 114.9 115.215966108286 -0.315966108286235 69 114.3 112.293695489412 2.00630451058809 70 128.9 135.140607702162 -6.24060770216161 71 138.9 136.017853368030 2.88214663196970 72 129.4 126.835678441447 2.56432155855299 73 115 122.698474815212 -7.69847481521242 74 128 122.679113817304 5.32088618269609 75 127 122.815828971345 4.18417102865494 76 128.8 139.113101039096 -10.3131010390959 77 137.9 129.363176372185 8.53682362781506 78 128.4 126.684242749948 1.71575725005195 79 135.9 141.633620625368 -5.73362062536772 80 122.2 117.862370635259 4.33762936474122 81 113.1 116.056216375513 -2.95621637551284 82 136.2 128.329397192501 7.87060280749886 83 138 127.366414154306 10.6335858456936 84 115.2 120.078978652133 -4.87897865213301 85 111 110.259893945383 0.740106054617501 86 99.2 106.797838825771 -7.59783882577088 87 102.4 106.489973367826 -4.08997336782605 88 112.7 113.675038105812 -0.975038105811883 89 105.5 107.142023410871 -1.64202341087054 90 98.3 104.886039763689 -6.58603976368855 91 116.4 113.762674861029 2.63732513897082 92 97.4 92.833330083389 4.56666991661094 93 93.3 94.2850719924787 -0.98507199247871 94 117.4 117.093325644812 0.306674355187901 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.350948087707447 0.701896175414895 0.649051912292553 20 0.261698532857612 0.523397065715224 0.738301467142388 21 0.149225385352350 0.298450770704700 0.85077461464765 22 0.082073379403211 0.164146758806422 0.91792662059679 23 0.0472657177173148 0.0945314354346295 0.952734282282685 24 0.0231051498557271 0.0462102997114542 0.976894850144273 25 0.115319968144366 0.230639936288732 0.884680031855634 26 0.0713366557646576 0.142673311529315 0.928663344235342 27 0.0431100275249279 0.0862200550498558 0.956889972475072 28 0.181867309483902 0.363734618967803 0.818132690516098 29 0.129328147553174 0.258656295106347 0.870671852446826 30 0.106164615170684 0.212329230341367 0.893835384829316 31 0.251529028540771 0.503058057081542 0.74847097145923 32 0.192607750692537 0.385215501385074 0.807392249307463 33 0.152293820402890 0.304587640805781 0.84770617959711 34 0.141030077059878 0.282060154119755 0.858969922940122 35 0.106006532501817 0.212013065003633 0.893993467498183 36 0.0867546028643802 0.173509205728760 0.91324539713562 37 0.095567114203011 0.191134228406022 0.904432885796989 38 0.0723064823293319 0.144612964658664 0.927693517670668 39 0.0498285438194527 0.0996570876389054 0.950171456180547 40 0.0431829325570652 0.0863658651141305 0.956817067442935 41 0.0286666214659606 0.0573332429319212 0.97133337853404 42 0.0227198673099715 0.0454397346199429 0.977280132690029 43 0.0176906388879799 0.0353812777759598 0.98230936111202 44 0.0405961200911694 0.0811922401823389 0.95940387990883 45 0.0272107057109999 0.0544214114219997 0.972789294289 46 0.0198052147185100 0.0396104294370199 0.98019478528149 47 0.102976110180437 0.205952220360874 0.897023889819563 48 0.082203239724916 0.164406479449832 0.917796760275084 49 0.0712288208390947 0.142457641678189 0.928771179160905 50 0.0502916426856375 0.100583285371275 0.949708357314363 51 0.0360062810883582 0.0720125621767164 0.963993718911642 52 0.070577293459297 0.141154586918594 0.929422706540703 53 0.0780916530640315 0.156183306128063 0.921908346935969 54 0.105993066819119 0.211986133638238 0.89400693318088 55 0.096771295514653 0.193542591029306 0.903228704485347 56 0.15623517346787 0.31247034693574 0.84376482653213 57 0.123630163253815 0.247260326507631 0.876369836746185 58 0.0918893921387205 0.183778784277441 0.90811060786128 59 0.0976803454062666 0.195360690812533 0.902319654593733 60 0.0894044465276738 0.178808893055348 0.910595553472326 61 0.0637831248729862 0.127566249745972 0.936216875127014 62 0.0428810017659313 0.0857620035318626 0.957118998234069 63 0.0288121713778732 0.0576243427557464 0.971187828622127 64 0.0217326378135419 0.0434652756270839 0.978267362186458 65 0.0231489624330761 0.0462979248661522 0.976851037566924 66 0.0211463957627954 0.0422927915255909 0.978853604237205 67 0.0138904217920223 0.0277808435840445 0.986109578207978 68 0.0103350311357447 0.0206700622714894 0.989664968864255 69 0.00620686184124721 0.0124137236824944 0.993793138158753 70 0.0208228438993429 0.0416456877986859 0.979177156100657 71 0.0459698379957532 0.0919396759915063 0.954030162004247 72 0.0264477252475938 0.0528954504951875 0.973552274752406 73 0.480976422994018 0.961952845988036 0.519023577005982 74 0.37635254483582 0.75270508967164 0.62364745516418 75 0.316415104023192 0.632830208046383 0.683584895976808 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 11 0.192982456140351 NOK 10% type I error level 23 0.403508771929825 NOK

Charts produced by software:

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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') }

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