Met dummy variabele (kredietcrisis)

*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: Wed, 17 Dec 2008 15:49:07 -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/17/t1229554304ctyaasva74iz3fg.htm/, Retrieved Wed, 17 Dec 2008 23:51:44 +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/2008/Dec/17/t1229554304ctyaasva74iz3fg.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}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

32,68 10967,87 0 31,54 10433,56 0 32,43 10665,78 0 26,54 10666,71 0 25,85 10682,74 0 27,6 10777,22 0 25,71 10052,6 0 25,38 10213,97 0 28,57 10546,82 0 27,64 10767,2 0 25,36 10444,5 0 25,9 10314,68 0 26,29 9042,56 0 21,74 9220,75 0 19,2 9721,84 0 19,32 9978,53 0 19,82 9923,81 0 20,36 9892,56 0 24,31 10500,98 0 25,97 10179,35 0 25,61 10080,48 0 24,67 9492,44 0 25,59 8616,49 0 26,09 8685,4 0 28,37 8160,67 0 27,34 8048,1 0 24,46 8641,21 0 27,46 8526,63 0 30,23 8474,21 0 32,33 7916,13 0 29,87 7977,64 0 24,87 8334,59 0 25,48 8623,36 0 27,28 9098,03 0 28,24 9154,34 0 29,58 9284,73 0 26,95 9492,49 0 29,08 9682,35 0 28,76 9762,12 0 29,59 10124,63 0 30,7 10540,05 0 30,52 10601,61 0 32,67 10323,73 0 33,19 10418,4 0 37,13 10092,96 0 35,54 10364,91 0 37,75 10152,09 0 41,84 10032,8 0 42,94 10204,59 0 49,14 10001,6 0 44,61 10411,75 0 40,22 10673,38 0 44,23 10539,51 0 45,85 10723,78 0 53,38 10682,06 0 53,26 10283,19 0 51,8 10377,18 0 55,3 10486,64 0 57,81 10545,38 0 63,96 10554,27 0 63,77 10532,54 0 59,15 10324,31 0 56,12 10695,25 0 57,42 10827,81 0 63,52 10872,48 0 61,71 10971,19 0 63,01 11145,65 0 68,18 11234,68 0 72,03 11333,88 0 69,75 10997,97 0 74,41 11036,89 0 74,33 11257,35 0 64,24 11533,59 0 60,03 11963,12 0 59,44 12185,15 0 62,5 12377,62 0 55,04 12512,89 0 58,34 12631,48 0 61,92 12268,53 0 67,65 12754,8 0 67,68 13407,75 0 70,3 13480,21 0 75,26 13673,28 1 71,44 13239,71 1 76,36 13557,69 1 81,71 13901,28 1 92,6 13200,58 1 90,6 13406,97 1 92,23 12538,12 1 94,09 12419,57 1 102,79 12193,88 1 109,65 12656,63 1 124,05 12812,48 1 132,69 12056,67 1 135,81 11322,38 1 116,07 11530,75 1 101,42 11114,08 1 75,73 9181,73 1 55,48 8614,55 1

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Olieprijs[t] = -11.4540263502318 + 0.00304979511034247DowJones[t] + 21.8149201426431`Dummy(kredietcrisis)`[t] -1.03364000649433M1[t] -4.0207911782665M2[t] -7.44361487608893M3[t] -5.44688097308302M4[t] -4.81218934617965M5[t] -4.15899005145394M6[t] -1.55249335180066M7[t] -0.647575188726108M8[t] + 1.36863510761649M9[t] + 2.37191696798508M10[t] + 1.91168058858591M11[t] + 0.551587294178185t + 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) -11.4540263502318 10.479944 -1.0929 0.277542 0.138771 DowJones 0.00304979511034247 0.001051 2.901 0.004748 0.002374 `Dummy(kredietcrisis)` 21.8149201426431 3.952547 5.5192 0 0 M1 -1.03364000649433 5.316734 -0.1944 0.846322 0.423161 M2 -4.0207911782665 5.318398 -0.756 0.451755 0.225877 M3 -7.44361487608893 5.314612 -1.4006 0.165019 0.082509 M4 -5.44688097308302 5.505062 -0.9894 0.325294 0.162647 M5 -4.81218934617965 5.496302 -0.8755 0.383781 0.191891 M6 -4.15899005145394 5.492622 -0.7572 0.451051 0.225526 M7 -1.55249335180066 5.485305 -0.283 0.777852 0.388926 M8 -0.647575188726108 5.491312 -0.1179 0.906407 0.453203 M9 1.36863510761649 5.502356 0.2487 0.804172 0.402086 M10 2.37191696798508 5.496506 0.4315 0.667187 0.333594 M11 1.91168058858591 5.467013 0.3497 0.727457 0.363729 t 0.551587294178185 0.058264 9.467 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.929260072014314 R-squared 0.863524281440048 Adjusted R-squared 0.840778328346723 F-TEST (value) 37.9638645123842 F-TEST (DF numerator) 14 F-TEST (DF denominator) 84 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 10.9333368282186 Sum Squared Residuals 10041.1797527396 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 32.68 21.5136772343240 11.1663227656760 2 31.54 17.4485773313230 14.0914226686770 3 32.43 15.2855643482024 17.1444356517976 4 26.54 17.8367218548391 8.70327814516086 5 25.85 19.0718889915395 6.77811100846052 6 27.6 20.5648202224685 7.03517977753148 7 25.71 21.5129616834436 4.19703831655636 8 25.38 23.4616125776523 1.91838742234766 9 28.57 27.0445344706506 1.52546552934938 10 27.64 29.2715174716147 -1.63151747161467 11 25.36 28.3786995042862 -3.01869950428617 12 25.9 26.6226818086538 -0.722681808653778 13 26.29 22.2609237405688 4.02907625943124 14 21.74 20.3688028536867 1.37119714631329 15 19.2 19.0257882818840 0.174211718116041 16 19.32 22.3569613859419 -3.03696138594187 17 19.82 23.3763555185855 -3.55635551858548 18 20.36 24.4858360102912 -4.12583601029118 19 24.31 29.4994763451572 -5.18947634515721 20 25.97 29.9750762010705 -4.00507620107051 21 25.61 32.2413405490317 -6.63134054903173 22 24.67 32.0028081868927 -7.33280818689271 23 25.59 29.4226910747672 -3.83269107476724 24 26.09 28.2727591614132 -2.18275916141322 25 28.37 26.1903874608471 2.17961253915294 26 27.34 23.4115081476818 3.92849185231817 27 24.46 22.3491357219328 2.11086427806719 28 27.46 24.5480113953739 2.91198860462614 29 30.23 25.5744200567713 4.65557994322874 30 32.33 25.0771769904952 7.25282300950477 31 29.87 28.4228538815639 1.44714611843613 32 24.87 30.9679837034533 -6.09798370345335 33 25.48 34.4164706279877 -8.93647062798773 34 27.28 37.4189860275608 -10.1389860275608 35 28.24 37.6820709050032 -9.44207090500317 36 29.58 36.719640395033 -7.139640395033 37 26.95 36.8712131148416 -9.9212131148416 38 29.08 35.0146833368972 -5.93468333689724 39 28.76 32.386729089205 -3.62672908920501 40 29.59 36.0406315118394 -6.45063151183936 41 30.7 38.4938563176594 -7.79385631765938 42 30.52 39.886388293556 -9.36638829355597 43 32.67 42.1969952221255 -9.52699522212546 44 33.19 43.9422247824743 -10.7522247824743 45 37.13 45.5174970522852 -8.38749705228524 46 35.54 47.9017579870897 -12.3617579870897 47 37.75 47.3440515064856 -9.59405150648559 48 41.84 45.6201481533651 -3.78014815336511 49 42.94 45.6620197430547 -2.7220197430547 50 49.14 42.6073779560123 6.53262204398771 51 44.61 40.987015016875 3.62298498312499 52 40.22 44.333254108778 -4.11325410877801 53 44.23 45.111256958438 -0.881256958438021 54 45.85 46.8780292923247 -1.02802929232472 55 53.38 49.9088758341527 3.47112416584731 56 53.26 50.1489095157431 3.11109048425686 57 51.8 53.003357348685 -1.20335734868501 58 55.3 54.8920570760099 0.407942923990133 59 57.81 55.1625529555704 2.64744704442961 60 63.96 53.8295723396936 10.1304276603064 61 63.77 53.2812475796297 10.4887524203703 62 59.15 50.2106248662091 8.93937513379087 63 56.12 48.4706794607953 7.64932053920467 64 57.42 51.4232814978064 5.99671850219359 65 63.52 52.745794766467 10.7742052335330 66 61.71 54.2516266307128 7.45837336928723 67 63.01 57.9417778794946 5.06822212050542 68 68.18 59.6698065954211 8.5101934045789 69 72.03 62.5401438608879 9.48985613911214 70 69.75 63.0705563399195 6.6794436600805 71 74.41 63.280605280393 11.1293947196069 72 74.33 62.5928698160114 11.7371301839886 73 64.24 62.9532925049763 1.28670749502371 74 60.03 61.8277071211277 -1.7977071211277 75 59.44 59.6336167258328 -0.193616725832796 76 62.5 62.7689319879045 -0.268931987904513 77 55.04 64.3677566935621 -9.32775669356209 78 58.34 65.9342184846015 -7.59421848460149 79 61.92 67.9853793431342 -6.06537934313416 80 67.65 70.9249086686931 -3.27490866869313 81 67.68 75.484069976512 -7.80406997651203 82 70.3 77.2599272847542 -6.95992728475423 83 75.26 99.7550222841301 -24.4950222841301 84 71.44 97.0726293237312 -25.6326293237312 85 76.36 97.5603504606018 -21.2003504606018 86 81.71 96.1726656849704 -14.4626656849704 87 92.6 91.1644378475092 1.43556215249083 88 90.6 94.3422062575168 -3.74220625751684 89 92.23 92.8786706969773 -0.648670696977324 90 94.09 93.7219040755501 0.368095924449881 91 102.79 96.1916798109284 6.59832018907161 92 109.65 99.0594779554921 10.5905220445079 93 124.05 102.102586113960 21.9474138860402 94 132.69 101.352389626159 31.3376103738414 95 135.81 99.2043064893643 36.6056935106357 96 116.07 98.4796990020986 17.5903009979014 97 101.42 96.726888161156 4.69311183884396 98 75.73 88.3980527020918 -12.6680527020918 99 55.48 83.7970335077635 -28.3170335077635 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.0152824071144878 0.0305648142289757 0.984717592885512 19 0.00614641528141479 0.0122928305628296 0.993853584718585 20 0.00287530311853686 0.00575060623707372 0.997124696881463 21 0.000733886144015351 0.00146777228803070 0.999266113855985 22 0.000339331042066048 0.000678662084132096 0.999660668957934 23 0.000435637402795624 0.000871274805591249 0.999564362597204 24 0.000243371072083235 0.000486742144166469 0.999756628927917 25 0.000151119076660528 0.000302238153321057 0.99984888092334 26 0.000108602116485887 0.000217204232971774 0.999891397883514 27 4.34460513516119e-05 8.68921027032238e-05 0.999956553948648 28 5.62373954537575e-05 0.000112474790907515 0.999943762604546 29 0.000102525102114243 0.000205050204228485 0.999897474897886 30 0.000112269961875788 0.000224539923751576 0.999887730038124 31 5.1896920522034e-05 0.000103793841044068 0.999948103079478 32 1.85294265222706e-05 3.70588530445412e-05 0.999981470573478 33 6.63630788487907e-06 1.32726157697581e-05 0.999993363692115 34 2.84476207348011e-06 5.68952414696022e-06 0.999997155237927 35 1.67465045610087e-06 3.34930091220174e-06 0.999998325349544 36 8.56571297798906e-07 1.71314259559781e-06 0.999999143428702 37 2.81396972670017e-07 5.62793945340033e-07 0.999999718603027 38 1.11307069938271e-07 2.22614139876543e-07 0.99999988869293 39 4.53523722519101e-08 9.07047445038201e-08 0.999999954647628 40 1.99235505956415e-08 3.98471011912831e-08 0.99999998007645 41 7.57731853433721e-09 1.51546370686744e-08 0.999999992422681 42 2.28723664517801e-09 4.57447329035603e-09 0.999999997712763 43 8.60543708999863e-10 1.72108741799973e-09 0.999999999139456 44 4.38129325712713e-10 8.76258651425426e-10 0.99999999956187 45 4.58419927493946e-10 9.16839854987893e-10 0.99999999954158 46 3.59941965750454e-10 7.19883931500908e-10 0.999999999640058 47 3.80681276184541e-10 7.61362552369082e-10 0.999999999619319 48 6.51356979660159e-10 1.30271395932032e-09 0.999999999348643 49 4.58214840751148e-10 9.16429681502295e-10 0.999999999541785 50 2.95458805640223e-09 5.90917611280445e-09 0.999999997045412 51 3.2887939419003e-09 6.5775878838006e-09 0.999999996711206 52 1.43907328943934e-09 2.87814657887867e-09 0.999999998560927 53 9.52652656630983e-10 1.90530531326197e-09 0.999999999047347 54 5.3234835451317e-10 1.06469670902634e-09 0.999999999467652 55 1.27985036728396e-09 2.55970073456793e-09 0.99999999872015 56 4.08411586283552e-09 8.16823172567103e-09 0.999999995915884 57 6.29433837353785e-09 1.25886767470757e-08 0.999999993705662 58 2.20669692225926e-08 4.41339384451852e-08 0.99999997793303 59 4.72651081291084e-08 9.45302162582168e-08 0.999999952734892 60 1.30178216693333e-07 2.60356433386666e-07 0.999999869821783 61 1.49538930490327e-07 2.99077860980654e-07 0.99999985046107 62 1.03692733236119e-07 2.07385466472237e-07 0.999999896307267 63 7.37318553500722e-08 1.47463710700144e-07 0.999999926268145 64 4.08803426816741e-08 8.17606853633482e-08 0.999999959119657 65 4.69083025183769e-08 9.38166050367539e-08 0.999999953091698 66 2.81387185745947e-08 5.62774371491895e-08 0.999999971861281 67 1.25518356050962e-08 2.51036712101923e-08 0.999999987448164 68 8.62726381537213e-09 1.72545276307443e-08 0.999999991372736 69 6.98953029873835e-09 1.39790605974767e-08 0.99999999301047 70 5.20094103532561e-09 1.04018820706512e-08 0.999999994799059 71 4.26242170289475e-09 8.5248434057895e-09 0.999999995737578 72 4.87552740283677e-09 9.75105480567354e-09 0.999999995124473 73 7.92860099444131e-09 1.58572019888826e-08 0.9999999920714 74 1.16154522714019e-07 2.32309045428039e-07 0.999999883845477 75 5.53235491770459e-06 1.10647098354092e-05 0.999994467645082 76 8.08321813107744e-05 0.000161664362621549 0.99991916781869 77 0.000150085697896294 0.000300171395792587 0.999849914302104 78 0.000237197390622856 0.000474394781245712 0.999762802609377 79 0.000465422953996819 0.000930845907993637 0.999534577046003 80 0.00456397999486808 0.00912795998973615 0.995436020005132 81 0.00694313223976411 0.0138862644795282 0.993056867760236 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 61 0.953125 NOK 5% type I error level 64 1 NOK 10% type I error level 64 1 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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