multiple regression with monthly dummies, index van de totale industrie zonder de bouwnijverheid, prijsindex van de industriele grondstoffen

*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 08:22:52 -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/t1258644278ldure3vda312i6v.htm/, Retrieved Thu, 19 Nov 2009 16:24:51 +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/t1258644278ldure3vda312i6v.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 «

97.6 82.9 96.9 83.8 105.6 86.2 102.8 86.1 101.7 86.2 104.2 88.8 92.7 89.6 91.9 87.8 106.5 88.3 112.3 88.6 102.8 91 96.5 91.5 101 95.4 98.9 98.7 105.1 99.9 103 98.6 99 100.3 104.3 100.2 94.6 100.4 90.4 101.4 108.9 103 111.4 109.1 100.8 111.4 102.5 114.1 98.2 121.8 98.7 127.6 113.3 129.9 104.6 128 99.3 123.5 111.8 124 97.3 127.4 97.7 127.6 115.6 128.4 111.9 131.4 107 135.1 107.1 134 100.6 144.5 99.2 147.3 108.4 150.9 103 148.7 99.8 141.4 115 138.9 90.8 139.8 95.9 145.6 114.4 147.9 108.2 148.5 112.6 151.1 109.1 157.5 105 167.5 105 172.3 118.5 173.5 103.7 187.5 112.5 205.5 116.6 195.1 96.6 204.5 101.9 204.5 116.5 201.7 119.3 207 115.4 206.6 108.5 210.6 111.5 211.1 108.8 215 121.8 223.9 109.6 238.2 112.2 238.9 119.6 229.6 104.1 232.2 105.3 222.1 115 221.6 124.1 227.3 116.8 221 107.5 213.6 115.6 243.4

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation tot_indus[t] = + 91.50941314779 + 0.0891604484025406prijsindex[t] -0.880632328668414M1[t] -2.81171827539424M2[t] + 7.7636909264908M3[t] -0.241785444105519M4[t] -0.737734760955868M5[t] + 7.38091200726559M6[t] -8.7761672856284M7[t] -7.53668625276632M8[t] + 8.06841293857287M9[t] + 9.47301803583064M10[t] + 4.10911971447549M11[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) 91.50941314779 1.587977 57.6264 0 0 prijsindex 0.0891604484025406 0.006837 13.0405 0 0 M1 -0.880632328668414 1.623658 -0.5424 0.589569 0.294784 M2 -2.81171827539424 1.687188 -1.6665 0.100824 0.050412 M3 7.7636909264908 1.68618 4.6043 2.2e-05 1.1e-05 M4 -0.241785444105519 1.685379 -0.1435 0.886407 0.443204 M5 -0.737734760955868 1.685179 -0.4378 0.663119 0.331559 M6 7.38091200726559 1.685698 4.3785 4.9e-05 2.4e-05 M7 -8.7761672856284 1.685218 -5.2077 2e-06 1e-06 M8 -7.53668625276632 1.68533 -4.4719 3.5e-05 1.8e-05 M9 8.06841293857287 1.685285 4.7876 1.1e-05 6e-06 M10 9.47301803583064 1.684963 5.6221 1e-06 0 M11 4.10911971447549 1.684939 2.4387 0.017715 0.008857 Multiple Linear Regression - Regression Statistics Multiple R 0.940618251649705 R-squared 0.884762695336547 Adjusted R-squared 0.861715234403857 F-TEST (value) 38.3887274142896 F-TEST (DF numerator) 12 F-TEST (DF denominator) 60 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.91838181167126 Sum Squared Residuals 511.017143921616 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 97.6 98.020181991692 -0.420181991691946 2 96.9 96.1693404485286 0.730659551471412 3 105.6 106.958734726580 -1.35873472657970 4 102.8 98.9443423111431 3.85565768885688 5 101.7 98.457309039133 3.24269096086698 6 104.2 106.807772973201 -2.60777297320109 7 92.7 90.7220220390291 1.97797796097087 8 91.9 91.8010142647666 0.0989857352333655 9 106.5 107.450693680307 -0.950693680307101 10 112.3 108.882046912086 3.41795308791435 11 102.8 103.732133666897 -0.932133666896585 12 96.5 99.6675941766224 -3.16759417662236 13 101 99.1346875967239 1.86531240327614 14 98.9 97.4978311297264 1.40216887027359 15 105.1 108.180232869694 -3.08023286969450 16 103 100.058847916175 2.94115208382512 17 99 99.7144713616088 -0.714471361608847 18 104.3 107.82420208499 -3.52420208499006 19 94.6 91.6849548817766 2.91504511822343 20 90.4 93.0135963630412 -2.61359636304119 21 108.9 108.761352271824 0.138647728175555 22 111.4 110.709836104338 0.690163895662286 23 100.8 105.551006814308 -4.75100681430841 24 102.5 101.682620310520 0.817379689480224 25 98.2 101.488523434551 -3.28852343455092 26 98.7 100.074568088560 -1.37456808855983 27 113.3 110.855046321771 2.44495367822928 28 104.6 102.680165099210 1.91983490079043 29 99.3 101.782993764548 -2.48299376454779 30 111.8 109.946220756971 1.85377924302948 31 97.3 94.0922869886452 3.20771301135483 32 97.7 95.3496001111878 2.35039988881225 33 115.6 111.026027661249 4.57397233875101 34 111.9 112.698114103714 -0.798114103714366 35 107 107.664109441449 -0.664109441448619 36 107.1 103.456913233730 3.64308676626966 37 100.6 103.512465613289 -2.9124656132886 38 99.2 101.83102892209 -2.63102892208988 39 108.4 112.727415738224 -4.32741573822406 40 103 104.525786381142 -1.52578638114216 41 99.8 103.378965790953 -3.57896579095327 42 115 111.274711438168 3.72528856183163 43 90.8 95.1978765488367 -4.39787654883667 44 95.9 96.9544881824335 -1.05448818243348 45 114.4 112.764656405099 1.63534359490148 46 108.2 114.222757771398 -6.02275777139781 47 112.6 109.090676615889 3.50932338411073 48 109.1 105.55218377119 3.54781622880996 49 105 105.563155926547 -0.563155926547026 50 105 104.060040132153 0.9399598678466 51 118.5 114.742441872121 3.75755812787852 52 103.7 107.985211779161 -4.28521177916073 53 112.5 109.094150533556 3.40584946644389 54 116.6 116.285528638391 0.314471361608847 55 96.6 100.966557560481 -4.36655756048104 56 101.9 102.206038593343 -0.306038593343112 57 116.5 117.561488529155 -1.06148852915520 58 119.3 119.438644002946 -0.138644002946441 59 115.4 114.039081502230 1.36091849776974 60 108.5 110.286603581365 -1.78660358136493 61 111.5 109.450551476898 2.04944852310221 62 108.8 107.867191278942 0.932808721058119 63 121.8 119.236128471610 2.56387152839047 64 109.6 112.505646513170 -2.90564651316954 65 112.2 112.072109510201 0.127890489799039 66 119.6 119.361564108279 0.238435891721199 67 104.1 103.436301981231 0.663698018768587 68 105.3 103.775262485228 1.52473751477217 69 115 119.335781452366 -4.33578145236576 70 124.1 121.248601105518 2.85139889448198 71 116.8 115.322991959227 1.47700804077315 72 107.5 110.554084926573 -3.05408492657256 73 115.6 112.330433960300 3.26956603970014 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0876504263239842 0.175300852647968 0.912349573676016 17 0.124866993131373 0.249733986262745 0.875133006868627 18 0.0614062384652999 0.122812476930600 0.9385937615347 19 0.0362099024328929 0.0724198048657858 0.963790097567107 20 0.0238467447764094 0.0476934895528189 0.97615325522359 21 0.0147371757379672 0.0294743514759344 0.985262824262033 22 0.0082730151896937 0.0165460303793874 0.991726984810306 23 0.00817329730206471 0.0163465946041294 0.991826702697935 24 0.0323566492829479 0.0647132985658959 0.967643350717052 25 0.0281514345271915 0.056302869054383 0.971848565472809 26 0.0151335243907255 0.030267048781451 0.984866475609274 27 0.0765286188178522 0.153057237635704 0.923471381182148 28 0.0613478980820767 0.122695796164153 0.938652101917923 29 0.055432119085644 0.110864238171288 0.944567880914356 30 0.105960127610053 0.211920255220107 0.894039872389947 31 0.111346452040612 0.222692904081224 0.888653547959388 32 0.122237559063594 0.244475118127188 0.877762440936406 33 0.227355380435044 0.454710760870087 0.772644619564956 34 0.205233764359241 0.410467528718481 0.79476623564076 35 0.168868954389584 0.337737908779169 0.831131045610416 36 0.236195945869751 0.472391891739503 0.763804054130249 37 0.237419589515036 0.474839179030071 0.762580410484964 38 0.231099356939852 0.462198713879704 0.768900643060148 39 0.370651506442558 0.741303012885116 0.629348493557442 40 0.381014125489478 0.762028250978956 0.618985874510522 41 0.435673843586824 0.871347687173647 0.564326156413177 42 0.543795253346215 0.91240949330757 0.456204746653785 43 0.609053585527041 0.781892828945918 0.390946414472959 44 0.533012669657136 0.933974660685728 0.466987330342864 45 0.573552475536687 0.852895048926625 0.426447524463313 46 0.863437274516533 0.273125450966934 0.136562725483467 47 0.871600631772682 0.256798736454636 0.128399368227318 48 0.975748348945464 0.0485033021090716 0.0242516510545358 49 0.970199915632048 0.0596001687359049 0.0298000843679524 50 0.947629410592909 0.104741178814183 0.0523705894070914 51 0.938433890602226 0.123132218795548 0.061566109397774 52 0.911086546980073 0.177826906039855 0.0889134530199273 53 0.943813063144107 0.112373873711786 0.056186936855893 54 0.911942545220667 0.176114909558666 0.0880574547793328 55 0.935682568733853 0.128634862532295 0.0643174312661474 56 0.878758430947258 0.242483138105484 0.121241569052742 57 0.951517083674759 0.096965832650483 0.0484829163252415 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 6 0.142857142857143 NOK 10% type I error level 11 0.261904761904762 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/10vaut1258644167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/10vaut1258644167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/1a0oc1258644167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/1a0oc1258644167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/29bq11258644167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/29bq11258644167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/35ofh1258644167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/35ofh1258644167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/4hzhw1258644167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/4hzhw1258644167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/5wezg1258644167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/5wezg1258644167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/6k0t61258644167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/6k0t61258644167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/7h4vc1258644167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/7h4vc1258644167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/8uced1258644167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/8uced1258644167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/9me811258644167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644278ldure3vda312i6v/9me811258644167.ps (open in new window)

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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