invoer - werkloosheid

*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, 07 Dec 2008 07:22:03 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/07/t12286598558uzb3woqki8j0pk.htm/, Retrieved Sun, 07 Dec 2008 14:24:15 +0000

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/2008/Dec/07/t12286598558uzb3woqki8j0pk.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

11554.5 7.5 13182.1 7.2 14800.1 6.9 12150.7 6.7 14478.2 6.4 13253.9 6.3 12036.8 6.8 12653.2 7.3 14035.4 7.1 14571.4 7.1 15400.9 6.8 14283.2 6.5 14485.3 6.3 14196.3 6.1 15559.1 6.1 13767.4 6.3 14634 6.3 14381.1 6 12509.9 6.2 12122.3 6.4 13122.3 6.8 13908.7 7.5 13456.5 7.5 12441.6 7.6 12953 7.6 13057.2 7.4 14350.1 7.3 13830.2 7.1 13755.5 6.9 13574.4 6.8 12802.6 7.5 11737.3 7.6 13850.2 7.8 15081.8 8 13653.3 8.1 14019.1 8.2 13962 8.3 13768.7 8.2 14747.1 8 13858.1 7.9 13188 7.6 13693.1 7.6 12970 8.2 11392.8 8.3 13985.2 8.4 14994.7 8.4 13584.7 8.4 14257.8 8.6 13553.4 8.9 14007.3 8.8 16535.8 8.3 14721.4 7.5 13664.6 7.2 16805.9 7.5 13829.4 8.8 13735.6 9.3 15870.5 9.3 15962.4 8.7 15744.1 8.2 16083.7 8.3 14863.9 8.5 15533.1 8.6 17473.1 8.6 15925.5 8.2 15573.7 8.1 17495 8 14155.8 8.6 14913.9 8.7 17250.4 8.8 15879.8 8.5 17647.8 8.4 17749.9 8.5 17111.8 8.7 16934.8 8.7 20280 8.6 16238.2 8.5 17896.1 8.3 18089.3 8.1 15660 8.2 16162.4 8.1 17850.1 8.1 18520.4 7.9 18524.7 7.9 16843.7 7.9

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 8 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation Invoer[t] = + 6897.17140456182 + 1032.35252100840Werkloosheid[t] -1057.36721488596M1[t] -625.726926770708M2[t] + 1417.79064825930M3[t] -239.643061224490M4[t] + 352.341728691476M5[t] + 1012.16690876351M6[t] -1481.77738895558M7[t] -1866.39075030012M8[t] -62.506680672269M9[t] + 389.1462484994M10[t] + 377.529393757503M11[t] + 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) 6897.17140456182 1771.678187 3.893 0.000221 0.000111 Werkloosheid 1032.35252100840 211.19157 4.8882 6e-06 3e-06 M1 -1057.36721488596 806.19446 -1.3116 0.193896 0.096948 M2 -625.726926770708 806.37509 -0.776 0.440341 0.22017 M3 1417.79064825930 807.998938 1.7547 0.083626 0.041813 M4 -239.643061224490 812.671828 -0.2949 0.768944 0.384472 M5 352.341728691476 819.07575 0.4302 0.668375 0.334187 M6 1012.16690876351 821.877003 1.2315 0.222188 0.111094 M7 -1481.77738895558 807.125398 -1.8359 0.070564 0.035282 M8 -1866.39075030012 806.177524 -2.3151 0.023502 0.011751 M9 -62.506680672269 806.448459 -0.0775 0.938437 0.469218 M10 389.1462484994 806.313003 0.4826 0.630847 0.315424 M11 377.529393757503 806.222686 0.4683 0.641026 0.320513 Multiple Linear Regression - Regression Statistics Multiple R 0.645875073329462 R-squared 0.417154610348338 Adjusted R-squared 0.318645530407212 F-TEST (value) 4.23468182423032 F-TEST (DF numerator) 12 F-TEST (DF denominator) 71 p-value 5.16180924434728e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1508.20948232195 Sum Squared Residuals 161503404.822176 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11554.5 13582.4480972389 -2027.9480972389 2 13182.1 13704.3826290516 -522.282629051621 3 14800.1 15438.1944477791 -638.094447779111 4 12150.7 13574.2902340936 -1423.59023409364 5 14478.2 13856.5692677071 621.630732292918 6 13253.9 14413.1591956783 -1159.25919567827 7 12036.8 12435.3911584634 -398.591158463385 8 12653.2 12566.9540576230 86.2459423769529 9 14035.4 14164.3676230492 -128.967623049219 10 14571.4 14616.0205522209 -44.6205522208887 11 15400.9 14294.6979411765 1106.20205882353 12 14283.2 13607.4627911164 675.737208883554 13 14485.3 12343.6250720288 2141.67492797119 14 14196.3 12568.7948559424 1627.50514405762 15 15559.1 14612.3124309724 946.787569027612 16 13767.4 13161.3492256903 606.050774309723 17 14634 13753.3340156062 880.665984393758 18 14381.1 14103.4534393758 277.64656062425 19 12509.9 11815.9796458583 693.920354141656 20 12122.3 11637.8367887155 484.463211284513 21 13122.3 13854.6618667467 -732.361866746699 22 13908.7 15028.9615606242 -1120.26156062425 23 13456.5 15017.3447058824 -1560.84470588235 24 12441.6 14743.0505642257 -2301.45056422569 25 12953 13685.6833493397 -732.683349339734 26 13057.2 13910.8531332533 -853.653133253301 27 14350.1 15851.1354561825 -1501.03545618247 28 13830.2 13987.231242497 -157.031242496998 29 13755.5 14372.7455282113 -617.245528211285 30 13574.4 14929.3354561825 -1354.93545618247 31 12802.6 13158.0379231693 -355.437923169267 32 11737.3 12876.6598139256 -1139.35981392557 33 13850.2 14887.0143877551 -1036.81438775510 34 15081.8 15545.1378211285 -463.337821128452 35 13653.3 15636.7562184874 -1983.45621848740 36 14019.1 15362.4620768307 -1343.36207683073 37 13962 14408.3301140456 -446.330114045618 38 13768.7 14736.7351500600 -968.035150060023 39 14747.1 16573.7822208884 -1826.68222088835 40 13858.1 14813.1132593037 -955.013259303722 41 13188 15095.3922929172 -1907.39229291717 42 13693.1 15755.2174729892 -2062.11747298920 43 12970 13880.6846878751 -910.68468787515 44 11392.8 13599.3065786315 -2206.50657863145 45 13985.2 15506.4259003601 -1521.22590036014 46 14994.7 15958.0788295318 -963.378829531812 47 13584.7 15946.4619747899 -2361.76197478992 48 14257.8 15775.4030852341 -1517.60308523409 49 13553.4 15027.7416266507 -1474.34162665066 50 14007.3 15356.1466626651 -1348.84666266507 51 16535.8 16883.4879771909 -347.687977190878 52 14721.4 14400.1722509004 321.227749099639 53 13664.6 14682.4512845138 -1017.85128451381 54 16805.9 15651.9822208884 1153.91777911165 55 13829.4 14500.0962004802 -670.696200480193 56 13735.6 14631.6590996399 -896.059099639857 57 15870.5 16435.5431692677 -565.043169267708 58 15962.4 16267.7845858343 -305.384585834333 59 15744.1 15739.9914705882 4.10852941176618 60 16083.7 15465.6973289316 618.002671068427 61 14863.9 14614.8006182473 249.099381752702 62 15533.1 15149.6761584634 383.423841536615 63 17473.1 17193.1937334934 279.906266506601 64 15925.5 15122.8190156062 802.680984393758 65 15573.7 15611.5685534214 -37.8685534213672 66 17495 16168.1584813926 1326.84151860744 67 14155.8 14293.6256962785 -137.825696278512 68 14913.9 14012.2475870348 901.652412965186 69 17250.4 15919.3669087635 1331.03309123649 70 15879.8 16061.3140816327 -181.514081632654 71 17647.8 15946.4619747899 1701.33802521008 72 17749.9 15672.1678331333 2077.73216686675 73 17111.8 14821.2711224490 2290.52887755102 74 16934.8 15252.9114105642 1681.88858943577 75 20280 17193.1937334934 3086.8062665066 76 16238.2 15432.5247719088 805.675228091237 77 17896.1 15818.0390576230 2078.06094237695 78 18089.3 16271.3937334934 1817.90626650660 79 15660 13880.6846878751 1779.31531212485 80 16162.4 13392.8360744298 2769.56392557023 81 17850.1 15196.7201440576 2653.37985594238 82 18520.4 15441.9025690276 3078.49743097239 83 18524.7 15430.2857142857 3094.41428571429 84 16843.7 15052.7563205282 1790.94367947179 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0916745941906073 0.183349188381215 0.908325405809393 17 0.0316586407061357 0.0633172814122714 0.968341359293864 18 0.0129478833983927 0.0258957667967854 0.987052116601607 19 0.00522381199302209 0.0104476239860442 0.994776188006978 20 0.0137235827636268 0.0274471655272535 0.986276417236373 21 0.0108574795959282 0.0217149591918563 0.989142520404072 22 0.00445681296627541 0.00891362593255082 0.995543187033725 23 0.00289602324348607 0.00579204648697214 0.997103976756514 24 0.00134233208281244 0.00268466416562488 0.998657667917188 25 0.000829627526762859 0.00165925505352572 0.999170372473237 26 0.000330593911038994 0.000661187822077987 0.999669406088961 27 0.000128451561454701 0.000256903122909402 0.999871548438545 28 0.000230037212857345 0.00046007442571469 0.999769962787143 29 8.82879579311043e-05 0.000176575915862209 0.999911712042069 30 4.13622935642553e-05 8.27245871285105e-05 0.999958637706436 31 5.92153714761074e-05 0.000118430742952215 0.999940784628524 32 2.36555022732584e-05 4.73110045465168e-05 0.999976344497727 33 1.74385337319129e-05 3.48770674638259e-05 0.999982561466268 34 1.90458459751631e-05 3.80916919503262e-05 0.999980954154025 35 1.08590778238173e-05 2.17181556476346e-05 0.999989140922176 36 1.52902749566242e-05 3.05805499132485e-05 0.999984709725043 37 2.25566410002336e-05 4.51132820004671e-05 0.999977443359 38 1.41394796174414e-05 2.82789592348828e-05 0.999985860520383 39 1.15919852463778e-05 2.31839704927556e-05 0.999988408014754 40 9.26250008857476e-06 1.85250001771495e-05 0.999990737499911 41 8.26509226910683e-06 1.65301845382137e-05 0.99999173490773 42 1.22565212568905e-05 2.45130425137810e-05 0.999987743478743 43 9.46324089004295e-06 1.89264817800859e-05 0.99999053675911 44 1.7885793696141e-05 3.5771587392282e-05 0.999982114206304 45 2.99175051394807e-05 5.98350102789613e-05 0.99997008249486 46 2.40923930576598e-05 4.81847861153196e-05 0.999975907606942 47 7.18296284716723e-05 0.000143659256943345 0.999928170371528 48 0.000129365047792768 0.000258730095585536 0.999870634952207 49 0.000166332237096653 0.000332664474193306 0.999833667762903 50 0.000215510627879166 0.000431021255758333 0.99978448937212 51 0.000942246491834982 0.00188449298366996 0.999057753508165 52 0.00140789768056405 0.00281579536112810 0.998592102319436 53 0.0146643939539420 0.0293287879078840 0.985335606046058 54 0.087484016938624 0.174968033877248 0.912515983061376 55 0.072442835129098 0.144885670258196 0.927557164870902 56 0.0660354109704707 0.132070821940941 0.93396458902953 57 0.0649769623546101 0.129953924709220 0.93502303764539 58 0.0516421145969793 0.103284229193959 0.94835788540302 59 0.104252798238282 0.208505596476563 0.895747201761718 60 0.125247539742063 0.250495079484127 0.874752460257937 61 0.200676634917091 0.401353269834182 0.799323365082909 62 0.213106543197046 0.426213086394093 0.786893456802954 63 0.443540722135186 0.887081444270372 0.556459277864814 64 0.391854029659576 0.783708059319151 0.608145970340424 65 0.668436357869271 0.663127284261458 0.331563642130729 66 0.628813379746923 0.742373240506154 0.371186620253077 67 0.538586362814517 0.922827274370966 0.461413637185483 68 0.40853354740351 0.81706709480702 0.59146645259649 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 31 0.584905660377358 NOK 5% type I error level 36 0.679245283018868 NOK 10% type I error level 37 0.69811320754717 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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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