Lineaire trend - Ws 8

*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, 30 Nov 2010 09:17:30 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb.htm/, Retrieved Tue, 30 Nov 2010 10:16:07 +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/2010/Nov/30/t1291108567elygo0fzcdcatfb.htm/}, year = {2010}, } @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 = {2010}, 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 «

2649.2 31077 0 2579.4 31293 0 2504.6 30236 0 2462.3 30160 0 2467.4 32436 0 2446.7 30695 0 2656.3 27525 0 2626.2 26434 0 2482.6 25739 0 2539.9 25204 0 2502.7 24977 0 2466.9 24320 0 2513.2 22680 1 2443.3 22052 1 2293.4 21467 1 2070.8 21383 1 2029.6 21777 1 2052 21928 1 1864.4 21814 1 1670.1 22937 1 1811 23595 1 1905.4 20830 1 1862.8 19650 1 2014.5 19195 1 2197.8 19644 1 2962.3 18483 0 3047 18079 0 3032.6 19178 0 3504.4 18391 0 3801.1 18441 0 3857.6 18584 0 3674.4 20108 0 3721 20148 0 3844.5 19394 0 4116.7 17745 0 4105.2 17696 0 4435.2 17032 0 4296.5 16438 0 4202.5 15683 0 4562.8 15594 0 4621.4 15713 0 4697 15937 0 4591.3 16171 0 4357 15928 0 4502.6 16348 0 4443.9 15579 0 4290.9 15305 0 4199.8 15648 0 4138.5 14954 0 3970.1 15137 0 3862.3 15839 0 3701.6 16050 0 3570.12 15168 0 3801.06 17064 0 3895.51 16005 0 3917.96 14886 0 3813.06 14931 0 3667.03 14544 0 3494.17 13812 0 3364 13031 0 3295.3 12574 0 3277.0 11964 0 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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Bel20[t] = + 7157.54976019622 -0.145896546986036Goudprijs[t] -1480.75533060803Crisis[t] -25.7971417400726t + 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) 7157.54976019622 1150.081548 6.2235 0 0 Goudprijs -0.145896546986036 0.039329 -3.7096 0.000428 0.000214 Crisis -1480.75533060803 196.975377 -7.5175 0 0 t -25.7971417400726 11.442122 -2.2546 0.027484 0.013742 Multiple Linear Regression - Regression Statistics Multiple R 0.794490138920613 R-squared 0.631214580842094 Adjusted R-squared 0.614451607244008 F-TEST (value) 37.6552869422966 F-TEST (DF numerator) 3 F-TEST (DF denominator) 66 p-value 2.65343302885412e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 526.977815912378 Sum Squared Residuals 18328570.8186095 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2649.2 2597.72562777112 51.4743722288777 2 2579.4 2540.41483188207 38.9851681179326 3 2504.6 2668.83034030624 -164.230340306235 4 2462.3 2654.1213361371 -191.821336137100 5 2467.4 2296.26365345681 171.136346543189 6 2446.7 2524.47240001943 -77.7724000194267 7 2656.3 2961.16731222509 -304.867312225086 8 2626.2 3094.54330324678 -468.343303246779 9 2482.6 3170.144261662 -687.544261662001 10 2539.9 3222.40177255946 -682.501772559457 11 2502.7 3229.72314698521 -727.023146985215 12 2466.9 3299.78003661497 -832.880036614967 13 2513.2 2032.49790132396 480.702098676036 14 2443.3 2098.32379109112 344.976208908879 15 2293.4 2157.87612933788 135.523870662120 16 2070.8 2144.33429754463 -73.5342975446341 17 2029.6 2061.05391629206 -31.4539162920635 18 2052 2013.2263959571 38.7736040429005 19 1864.4 2004.06146057344 -139.661460573435 20 1670.1 1814.42249656804 -144.322496568045 21 1811 1692.62542691116 118.374573088839 22 1905.4 2070.23223758748 -164.832237587476 23 1862.8 2216.59302129093 -353.793021290926 24 2014.5 2257.1788084295 -242.678808429499 25 2197.8 2165.87411709270 31.9258829073037 26 2962.3 3790.21819701144 -827.91819701144 27 3047 3823.36326025373 -776.363260253726 28 3032.6 3637.225813376 -604.625813376 29 3504.4 3726.24925411394 -221.849254113938 30 3801.1 3693.15728502456 107.942714975437 31 3857.6 3646.49693706549 211.103062934512 32 3674.4 3398.35345771870 276.046542281303 33 3721 3366.72045409918 354.279545900817 34 3844.5 3450.92930878658 393.570691213419 35 4116.7 3665.71557302648 450.984426973519 36 4105.2 3647.06736208872 458.132637911276 37 4435.2 3718.14552754738 717.054472452621 38 4296.5 3779.01093471701 517.489065282988 39 4202.5 3863.36568595140 339.134314048604 40 4562.8 3850.55333689308 712.24666310692 41 4621.4 3807.39450606167 814.00549393833 42 4697 3748.91653779672 948.083462203275 43 4591.3 3688.97960406192 902.32039593808 44 4357 3698.63532323945 658.364676760546 45 4502.6 3611.56163176525 891.038368234754 46 4443.9 3697.95893465743 745.941065342565 47 4290.9 3712.13744679154 578.762553208463 48 4199.8 3636.29778943525 563.502210564747 49 4138.5 3711.75285130349 426.747148696511 50 3970.1 3659.25664146497 310.843358535028 51 3862.3 3531.0401237407 331.259876259298 52 3701.6 3474.45881058658 227.141189413423 53 3570.12 3577.34242328819 -7.22242328818742 54 3801.06 3274.92542846259 526.134571537409 55 3895.51 3403.63272998073 491.87727001927 56 3917.96 3541.09382431803 376.866175681969 57 3813.06 3508.73133796359 304.328662036413 58 3667.03 3539.39615990711 127.633840092890 59 3494.17 3620.39529056082 -126.225290560816 60 3364 3708.54335201684 -344.543352016837 61 3295.3 3749.42093224938 -454.120932249382 62 3277 3812.62068417079 -535.620684170791 63 3257.2 3861.66847103456 -604.468471034555 64 3161.7 3851.19046672802 -689.490466728016 65 3097.3 3824.37204915904 -727.072049159041 66 3061.3 3893.55355950688 -832.253559506878 67 3119.3 3875.92662439802 -756.626624398023 68 3106.22 3863.26017188669 -757.040171886694 69 3080.58 3851.03140901632 -770.451409016322 70 2981.85 3833.40447390747 -851.554473907468 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.00580009291247104 0.0116001858249421 0.994199907087529 8 0.000785353057328181 0.00157070611465636 0.999214646942672 9 0.000282828266318656 0.000565656532637311 0.999717171733681 10 4.78183880289849e-05 9.56367760579698e-05 0.99995218161197 11 9.95780106716747e-06 1.99156021343349e-05 0.999990042198933 12 3.96387082263338e-06 7.92774164526676e-06 0.999996036129177 13 5.85938142354838e-07 1.17187628470968e-06 0.999999414061858 14 1.03537454062174e-07 2.07074908124349e-07 0.999999896462546 15 1.09253825038292e-07 2.18507650076583e-07 0.999999890746175 16 1.23127066435862e-06 2.46254132871725e-06 0.999998768729336 17 8.13040268361759e-07 1.62608053672352e-06 0.999999186959732 18 1.87348101574703e-07 3.74696203149406e-07 0.999999812651898 19 8.9730078333395e-08 1.7946015666679e-07 0.999999910269922 20 3.80969970527591e-08 7.61939941055182e-08 0.999999961903003 21 2.08793620137376e-08 4.17587240274751e-08 0.999999979120638 22 5.43255836247252e-09 1.08651167249450e-08 0.999999994567442 23 1.19739306867834e-09 2.39478613735668e-09 0.999999998802607 24 7.55423520161206e-10 1.51084704032241e-09 0.999999999244576 25 1.67730668969477e-08 3.35461337938953e-08 0.999999983226933 26 3.57139442792882e-06 7.14278885585764e-06 0.999996428605572 27 2.89756394780904e-05 5.79512789561808e-05 0.999971024360522 28 0.000347138311352434 0.000694276622704868 0.999652861688648 29 0.00892067526007236 0.0178413505201447 0.991079324739928 30 0.088288045727472 0.176576091454944 0.911711954272528 31 0.249048218773753 0.498096437547506 0.750951781226247 32 0.443480549954517 0.886961099909034 0.556519450045483 33 0.713180586768774 0.573638826462452 0.286819413231226 34 0.933939910580794 0.132120178838413 0.0660600894192063 35 0.984982206916234 0.0300355861675315 0.0150177930837657 36 0.998614113281174 0.0027717734376518 0.0013858867188259 37 0.999474592566515 0.00105081486697082 0.000525407433485409 38 0.999847728502987 0.000304542994025144 0.000152271497012572 39 0.999992495514672 1.50089706566349e-05 7.50448532831747e-06 40 0.99999242974898 1.51405020419357e-05 7.57025102096786e-06 41 0.999988123872897 2.37522542059033e-05 1.18761271029517e-05 42 0.999986657263771 2.66854724571332e-05 1.33427362285666e-05 43 0.99997804394477 4.39121104601649e-05 2.19560552300824e-05 44 0.99994917894397 0.000101642112060516 5.08210560302579e-05 45 0.99993229834514 0.000135403309718812 6.77016548594058e-05 46 0.999940642156747 0.000118715686506010 5.93578432530048e-05 47 0.99993716541425 0.000125669171499644 6.2834585749822e-05 48 0.99993646792641 0.000127064147179854 6.35320735899268e-05 49 0.999980130251419 3.97394971619784e-05 1.98697485809892e-05 50 0.99999173892872 1.65221425617821e-05 8.26107128089107e-06 51 0.99998777656591 2.44468681790151e-05 1.22234340895075e-05 52 0.999987823781038 2.43524379247331e-05 1.21762189623665e-05 53 0.99999487711494 1.02457701203298e-05 5.12288506016489e-06 54 0.99999910204845 1.79590310114643e-06 8.97951550573213e-07 55 0.999996697792395 6.60441521057183e-06 3.30220760528591e-06 56 0.999999157393126 1.68521374712177e-06 8.42606873560887e-07 57 0.999999268873319 1.46225336293918e-06 7.3112668146959e-07 58 0.999998865890568 2.26821886360416e-06 1.13410943180208e-06 59 0.999996271734494 7.45653101205344e-06 3.72826550602672e-06 60 0.999979268732372 4.14625352562967e-05 2.07312676281483e-05 61 0.999868089520067 0.000263820959866972 0.000131910479933486 62 0.999316515780306 0.00136696843938724 0.00068348421969362 63 0.997654044062178 0.00469191187564397 0.00234595593782198 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 49 0.859649122807018 NOK 5% type I error level 52 0.912280701754386 NOK 10% type I error level 52 0.912280701754386 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/10osin1291108641.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/10osin1291108641.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/1aj2w1291108641.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/1aj2w1291108641.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/2aj2w1291108641.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/2aj2w1291108641.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/3aj2w1291108641.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/3aj2w1291108641.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/4la1z1291108641.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/4la1z1291108641.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/5la1z1291108641.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/5la1z1291108641.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/6la1z1291108641.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/6la1z1291108641.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/7e1ik1291108641.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/7e1ik1291108641.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/8osin1291108641.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/8osin1291108641.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/9osin1291108641.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291108567elygo0fzcdcatfb/9osin1291108641.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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