paper multiple regressie 3 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 08:28:09 -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/t12620141309hcx4ygvffh85q0.htm/, Retrieved Mon, 28 Dec 2009 16:29:02 +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/t12620141309hcx4ygvffh85q0.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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Dataseries X:

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98.3 0 91.6 104.6 111.6 97.7 0 98.3 91.6 104.6 106.3 0 97.7 98.3 91.6 102.3 0 106.3 97.7 98.3 106.6 0 102.3 106.3 97.7 108.1 0 106.6 102.3 106.3 93.8 0 108.1 106.6 102.3 88.2 0 93.8 108.1 106.6 108.9 0 88.2 93.8 108.1 114.2 0 108.9 88.2 93.8 102.5 0 114.2 108.9 88.2 94.2 0 102.5 114.2 108.9 97.4 0 94.2 102.5 114.2 98.5 0 97.4 94.2 102.5 106.5 0 98.5 97.4 94.2 102.9 0 106.5 98.5 97.4 97.1 0 102.9 106.5 98.5 103.7 0 97.1 102.9 106.5 93.4 0 103.7 97.1 102.9 85.8 0 93.4 103.7 97.1 108.6 0 85.8 93.4 103.7 110.2 0 108.6 85.8 93.4 101.2 0 110.2 108.6 85.8 101.2 0 101.2 110.2 108.6 96.9 0 101.2 101.2 110.2 99.4 0 96.9 101.2 101.2 118.7 0 99.4 96.9 101.2 108.0 0 118.7 99.4 96.9 101.2 0 108.0 118.7 99.4 119.9 0 101.2 108.0 118.7 94.8 0 119.9 101.2 108.0 95.3 0 94.8 119.9 101.2 118.0 0 95.3 94.8 119.9 115.9 0 118.0 95.3 94.8 111.4 0 115.9 118.0 95.3 108.2 0 111.4 115.9 118.0 108.8 0 108.2 111.4 115.9 109.5 0 108.8 108.2 111.4 124.8 0 109.5 108.8 108.2 115.3 0 124.8 109.5 108.8 109 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] = + 19.9869861030992 -11.4392342516194dummy[t] -0.0153629225854587y1[t] + 0.252785801061932y2[t] + 0.436717755047556y3[t] + 2.46239864370432M1[t] + 8.5760007948152M2[t] + 23.4911860732244M3[t] + 15.3944276119058M4[t] + 10.2122519761235M5[t] + 16.4686381722918M6[t] -0.168390336112841M7[t] -4.48865165693964M8[t] + 18.7822099556609M9[t] + 29.5701203060938M10[t] + 16.7070692222148M11[t] + 0.149834959699562t + 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) 19.9869861030992 9.297621 2.1497 0.034762 0.017381 dummy -11.4392342516194 2.358966 -4.8493 6e-06 3e-06 y1 -0.0153629225854587 0.099791 -0.154 0.878057 0.439028 y2 0.252785801061932 0.089241 2.8326 0.005908 0.002954 y3 0.436717755047556 0.092919 4.7 1.1e-05 6e-06 M1 2.46239864370432 2.419917 1.0176 0.312118 0.156059 M2 8.5760007948152 2.500228 3.4301 0.000978 0.000489 M3 23.4911860732244 2.540249 9.2476 0 0 M4 15.3944276119058 2.892095 5.3229 1e-06 1e-06 M5 10.2122519761235 2.459896 4.1515 8.5e-05 4.3e-05 M6 16.4686381722918 2.354784 6.9937 0 0 M7 -0.168390336112841 2.688842 -0.0626 0.950229 0.475115 M8 -4.48865165693964 2.538528 -1.7682 0.081039 0.040519 M9 18.7822099556609 3.023327 6.2124 0 0 M10 29.5701203060938 3.456738 8.5543 0 0 M11 16.7070692222148 3.300871 5.0614 3e-06 1e-06 t 0.149834959699562 0.042771 3.5032 0.000774 0.000387 Multiple Linear Regression - Regression Statistics Multiple R 0.955143286174588 R-squared 0.912298697124391 Adjusted R-squared 0.893835264940052 F-TEST (value) 49.4111110012489 F-TEST (DF numerator) 16 F-TEST (DF denominator) 76 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.27202823081276 Sum Squared Residuals 1387.01711556945 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 98.3 96.3710722520604 1.92892774793964 2 97.7 96.1883380824101 1.51166191758987 3 106.3 107.278910125567 -0.978910125566887 4 102.3 101.974202967894 0.325797032105604 5 106.6 98.9152412182577 7.68475878174234 6 108.1 108.000031296169 0.0999687038308122 7 93.8 90.829901287962 2.97009871203797 8 88.2 89.1362297681043 -0.936229768104264 9 108.9 109.683198384269 -0.783198384268638 10 114.2 112.642266813755 1.55773318624480 11 102.5 102.634673853589 -0.134673853588572 12 94.2 96.6370080604358 -2.43700806043581 13 97.4 98.7337641506264 -1.33376415062643 14 98.5 97.740320026293 0.759679973707028 15 106.5 109.972598246061 -3.47259824606125 16 102.9 103.578332561079 -0.678332561078777 17 97.1 101.103974345352 -4.00397434535152 18 103.7 110.183013608772 -6.48301360877245 19 93.4 90.556083206673 2.84391679332703 20 85.8 85.6793182559089 0.120681744091103 21 108.6 109.495416472234 -0.895416472234476 22 110.2 113.663522182358 -3.46352218235792 23 101.2 103.370186707892 -2.17018670789242 24 101.2 97.3128408454296 3.88715915457036 25 96.9 98.3487506473522 -1.44875064735223 26 99.4 100.747788529852 -1.34778852985214 27 118.7 114.687422516931 4.01257748306902 28 108 105.198072765363 2.80192723463711 29 101.2 106.300675709059 -5.10067570905878 30 119.9 118.535209339563 1.36479066043717 31 94.8 95.3689057122798 -0.56890571227976 32 95.3 93.3415024535823 1.95849754641773 33 118 118.576215977324 -0.576215977324466 34 115.9 118.330000193604 -2.43000019360428 35 111.4 111.605642768484 -0.205642768483977 36 108.2 104.500184514953 3.69981548504725 37 108.8 105.106936080252 3.69306391974846 38 109.5 108.587010976399 0.912989023601475 39 124.8 122.395451833182 2.40454816681753 40 115.3 114.652456329778 0.647543670222247 41 109.5 113.939388603038 -4.43938860303777 42 124.2 124.715031252040 -0.515031252040463 43 92.9 102.387026422218 -9.48702642221821 44 98.4 99.8804478343504 -1.48044783435038 45 120.9 121.724203758391 -0.824203758391108 46 111.7 120.037339483203 -8.33733948320283 47 116.1 115.555090423465 0.544909576535312 48 109.4 106.430779420374 2.96922057962636 49 111.7 106.240398783335 5.45960121666492 50 114.3 112.696394427293 1.60360557270673 51 133.7 125.376869450304 8.3231305496963 52 114.3 118.793599169897 -4.4935991698971 53 126.5 120.098809895697 6.40119010430258 54 131 129.885883303344 1.11411669665627 55 104 107.941218928037 -3.94121892803712 56 108.9 110.651084193076 -1.75108419307613 57 128.5 129.136515713749 -0.63651571374936 58 132.4 129.220418780126 3.17958121987377 59 128 123.541805958410 4.45819404158955 60 116.4 116.597701178345 -0.197701178344835 61 120.9 119.979086403753 0.92091359624701 62 118.6 121.319516948401 -2.71951694840123 63 133.1 132.491482054684 0.608517945316374 64 121.1 125.705618730847 -4.60561873084694 65 127.6 123.518576404578 4.08142359542161 66 135.4 133.123916399087 2.27608360091293 67 114.9 112.919386700547 1.98061329945266 68 114.3 113.874294908514 0.425705091485807 69 128.9 135.528498801967 -6.62849880196692 70 138.9 137.137559983240 1.76244001676042 71 129.4 127.699356675681 1.70064332431871 72 115 120.192007412042 -5.19200741204152 73 128 124.991179541063 3.0088204589368 74 127 123.265964450019 3.73403554998091 75 128.8 135.343827351834 -6.54382735183364 76 137.9 132.793795604117 5.10620439588301 77 128.4 127.639949019371 0.760050980629402 78 135.9 137.278560688549 -1.37856068854941 79 122.2 122.248811681298 -0.0488116812978475 80 113.1 116.035932194604 -2.93593219460411 81 136.2 127.969414799121 8.23058520087926 82 138 130.268892563714 7.73110743628605 83 115.2 119.393243612479 -4.19324361247861 84 111 113.729478568422 -2.72947856842180 85 99.2 111.428812141558 -12.2288121415582 86 102.4 106.854666559333 -4.45466655933264 87 112.7 117.053438421437 -4.35343842143744 88 105.5 104.603921871025 0.896078128974846 89 98.3 103.683384804648 -5.38338480464786 90 116.4 112.878354112475 3.52164588752515 91 97.4 91.1486660609847 6.25133393901528 92 93.3 88.7011903918598 4.59880960814024 93 117.4 115.286536092944 2.1134639070557 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.406527313576166 0.813054627152332 0.593472686423834 21 0.256146499147762 0.512292998295524 0.743853500852238 22 0.160772738945639 0.321545477891279 0.83922726105436 23 0.0882206113968473 0.176441222793695 0.911779388603153 24 0.173344134021789 0.346688268043579 0.826655865978211 25 0.105172897640004 0.210345795280008 0.894827102359996 26 0.0815975417852673 0.163195083570535 0.918402458214733 27 0.198340679638792 0.396681359277583 0.801659320361208 28 0.145770417113246 0.291540834226492 0.854229582886754 29 0.119025805583764 0.238051611167527 0.880974194416236 30 0.104719837077727 0.209439674155454 0.895280162922273 31 0.103862715739732 0.207725431479464 0.896137284260268 32 0.107370676907294 0.214741353814589 0.892629323092706 33 0.0735470095776925 0.147094019155385 0.926452990422308 34 0.0494514903407963 0.0989029806815926 0.950548509659204 35 0.0315276651986831 0.0630553303973661 0.968472334801317 36 0.0228416777794757 0.0456833555589515 0.977158322220524 37 0.0209215556688549 0.0418431113377098 0.979078444331145 38 0.0126166938039519 0.0252333876079038 0.987383306196048 39 0.00787471215136968 0.0157494243027394 0.99212528784863 40 0.00489895753663155 0.0097979150732631 0.995101042463368 41 0.00490218758265247 0.00980437516530494 0.995097812417348 42 0.00272808470031292 0.00545616940062583 0.997271915299687 43 0.0257245584359396 0.0514491168718791 0.97427544156406 44 0.0170343925407213 0.0340687850814426 0.982965607459279 45 0.0108473172539900 0.0216946345079799 0.98915268274601 46 0.0303436640079129 0.0606873280158259 0.969656335992087 47 0.0221307442253014 0.0442614884506028 0.977869255774699 48 0.0153949633416543 0.0307899266833085 0.984605036658346 49 0.0245120604843922 0.0490241209687844 0.975487939515608 50 0.0162735793788318 0.0325471587576636 0.983726420621168 51 0.0518290764114912 0.103658152822982 0.94817092358851 52 0.0508610132976751 0.101722026595350 0.949138986702325 53 0.0705954880074617 0.141190976014923 0.929404511992538 54 0.0497544646709489 0.0995089293418978 0.950245535329051 55 0.0529281857357018 0.105856371471404 0.947071814264298 56 0.0428107550188381 0.0856215100376763 0.957189244981162 57 0.0300053918600412 0.0600107837200824 0.96999460813996 58 0.0309892828952915 0.061978565790583 0.969010717104708 59 0.0254794399145444 0.0509588798290887 0.974520560085456 60 0.0181671681794541 0.0363343363589081 0.981832831820546 61 0.0135464248124838 0.0270928496249677 0.986453575187516 62 0.0100072176962980 0.0200144353925961 0.989992782303702 63 0.00829949532394375 0.0165989906478875 0.991700504676056 64 0.0108509389528317 0.0217018779056634 0.989149061047168 65 0.0115116108471889 0.0230232216943777 0.988488389152811 66 0.0068879821009561 0.0137759642019122 0.993112017899044 67 0.00398586451181957 0.00797172902363915 0.99601413548818 68 0.00197686072621077 0.00395372145242155 0.99802313927379 69 0.0220169311808791 0.0440338623617581 0.977983068819121 70 0.0484012755069338 0.0968025510138675 0.951598724493066 71 0.0338581829876852 0.0677163659753703 0.966141817012315 72 0.373504641861841 0.747009283723681 0.62649535813816 73 0.246324882278811 0.492649764557623 0.753675117721189 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.0925925925925926 NOK 5% type I error level 23 0.425925925925926 NOK 10% type I error level 34 0.62962962962963 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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