Personal Standards (Yt) - Maandelijkse effecten

*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 10:58:46 +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/t12911146983v1rwfded32rlpa.htm/, Retrieved Tue, 30 Nov 2010 11:58:18 +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/t12911146983v1rwfded32rlpa.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 «

24 24 25 25 30 17 19 18 22 18 22 16 25 20 23 16 17 18 21 17 19 23 19 30 15 23 16 18 23 15 27 12 22 21 14 15 22 20 23 31 23 27 21 34 19 21 18 31 20 19 23 16 25 20 19 21 24 22 22 17 25 24 26 25 29 26 32 25 25 17 29 32 28 33 17 13 28 32 29 25 26 29 25 22 14 18 25 17 26 20 20 15 18 20 32 33 25 29 25 23 23 26 21 18 20 20 15 11 30 28 24 26 26 22 24 17 22 12 14 14 24 17 24 21 24 19 24 18 19 10 31 29 22 31 27 19 19 9 25 20 20 28 21 19 27 30 23 29 25 26 20 23 21 13 22 21 23 19 25 28 25 23 17 18 19 21 25 20 19 23 20 21 26 21 23 15 27 28 17 19 17 26 19 10 17 16 22 22 21 19 32 31 21 31 21 29 18 19 18 22 23 23 19 15 20 20 21 18 20 23 17 25 18 21 19 24 22 25 15 17 14 13 18 28 24 21 35 25 29 9 21 16 25 19 20 17 22 25 13 20 26 29 17 14 25 22 20 15 19 19 21 20 22 15 24 20 21 18 26 33 24 22 16 16 23 17 18 16 16 21 26 26 19 18 21 18 21 17 22 22 23 30 29 30 21 24 21 21 23 21 27 29 25 3 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation PS[t] = + 13.7057371438104 + 0.324108497176835x[t] + 1.14327246228138M1[t] + 0.439706450988726M2[t] + 1.83383716930753M3[t] + 1.71023841287010M4[t] + 1.09272498914167M5[t] + 1.90756098740434M6[t] + 2.40680372186540M7[t] + 2.79780553151221M8[t] + 2.16600472664307M9[t] + 1.47795465102299M10[t] + 0.411061338553018M11[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) 13.7057371438104 1.711689 8.0071 0 0 x 0.324108497176835 0.056197 5.7674 0 0 M1 1.14327246228138 1.495494 0.7645 0.445816 0.222908 M2 0.439706450988726 1.501932 0.2928 0.770121 0.38506 M3 1.83383716930753 1.495763 1.226 0.222164 0.111082 M4 1.71023841287010 1.5348 1.1143 0.266978 0.133489 M5 1.09272498914167 1.53657 0.7111 0.478129 0.239064 M6 1.90756098740434 1.541815 1.2372 0.217992 0.108996 M7 2.40680372186540 1.531582 1.5714 0.118243 0.059121 M8 2.79780553151221 1.527569 1.8315 0.069058 0.034529 M9 2.16600472664307 1.523508 1.4217 0.15724 0.07862 M10 1.47795465102299 1.52233 0.9709 0.333229 0.166614 M11 0.411061338553018 1.528798 0.2689 0.788403 0.394201 Multiple Linear Regression - Regression Statistics Multiple R 0.467233839799462 R-squared 0.218307461053750 Adjusted R-squared 0.154058759222551 F-TEST (value) 3.39785014843275 F-TEST (DF numerator) 12 F-TEST (DF denominator) 146 p-value 0.00021079102177457 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.87855041641 Sum Squared Residuals 2196.30038656459 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 22.6276135383358 1.37238646166418 2 25 22.2481560242200 2.75184397578002 3 30 21.0494187651241 8.9505812348759 4 19 21.2499285058635 -2.24992850586349 5 22 20.6324150821351 1.36758491786493 6 22 20.7990340860441 1.20096591395593 7 25 22.5947108092125 2.40528919078753 8 23 21.6892786301519 1.31072136984806 9 17 21.7056948196365 -4.70569481963647 10 21 20.6935362468396 0.306463753160447 11 19 21.5712939174306 -2.57129391743060 12 19 23.4289920591154 -4.42899205911543 13 15 22.3035050411590 -7.30350504115896 14 16 19.9793965439821 -3.97939654398212 15 23 20.4012017707704 2.59879822922958 16 27 19.3052775228025 7.69472247719752 17 22 21.6047405736656 0.395259426334421 18 14 20.4749255888672 -6.47492558886723 19 22 22.5947108092125 -0.594710809212473 20 23 26.5509060878045 -3.55090608780447 21 23 24.622671294228 -1.62267129422799 22 21 26.2033806988458 -5.20338069884575 23 19 20.9230769230769 -1.92307692307692 24 18 23.7531005562923 -5.75310055629226 25 20 21.0070710524516 -1.00707105245162 26 23 19.3311795496285 3.66882045037155 27 25 22.0217442566546 2.9782557433454 28 19 22.222253997394 -3.222253997394 29 24 21.9288490708424 2.07115092915758 30 22 21.1231425832209 0.876857416779095 31 25 23.8911447979198 1.10885520208018 32 26 24.6062551047435 1.39374489525654 33 29 24.2985627970511 4.70143720294885 34 32 23.2864042242542 8.71359577574576 35 25 19.6266429343696 5.37335706563042 36 29 24.0772090534691 4.9227909465309 37 28 25.5445900129273 2.45540998707269 38 17 18.3588540580979 -1.35885405809794 39 28 25.9110462227766 2.08895377722337 40 29 23.5186879861013 5.48131201389865 41 26 24.1976085510803 1.80239144891974 42 25 22.7436850691051 2.25631493089492 43 14 21.9464938148588 -7.9464938148588 44 25 22.0133871273288 2.98661287267123 45 26 22.3539118139901 3.64608818600986 46 20 20.0453192524859 -0.0453192524858794 47 18 20.5989684259001 -2.59896842590009 48 32 24.4013175506459 7.59868244935407 49 25 24.2481560242200 0.751843975780029 50 25 21.5999390298663 3.4000609701337 51 23 23.9663952397156 -0.966395239715616 52 21 21.2499285058635 -0.249928505863495 53 20 21.2806320764887 -1.28063207648874 54 15 19.1784916001599 -4.17849160015989 55 30 25.1875787866272 4.81242121337284 56 24 24.9303636019203 -0.930363601920293 57 26 23.0021288083438 2.99787119165619 58 24 20.6935362468395 3.30646375316045 59 22 18.0061004484854 3.9938995515146 60 14 18.2432561042861 -4.24325610428605 61 24 20.3588540580979 3.64114594190206 62 24 20.9517220355126 3.04827796448737 63 24 21.6976357594778 2.30236424052223 64 24 21.2499285058635 2.75007149413650 65 19 18.0395471047204 0.960452895279612 66 31 25.0124445493429 5.98755545065707 67 22 26.1599042781577 -4.15990427815767 68 27 22.6616041216824 4.33839587831756 69 19 18.7887183450449 0.211281654955056 70 25 21.6658617383701 3.33413826162994 71 20 23.1918364033148 -3.19183640331477 72 21 19.8637985901702 1.13620140982977 73 27 24.5722645213968 2.42773547860319 74 23 23.5445900129273 -0.544590012927316 75 25 23.9663952397156 1.03360476028438 76 20 22.8704709917477 -2.87047099174767 77 21 19.0118725962509 1.98812740374911 78 22 22.4195765719282 -0.419576571928247 79 23 22.2706023120356 0.729397687964362 80 25 25.5785805962740 -0.578580596273965 81 25 23.3262373055206 1.67376269447936 82 17 21.0176447440164 -4.01764474401639 83 19 20.9230769230769 -1.92307692307692 84 25 20.1879070873471 4.81209291265293 85 19 22.3035050411590 -3.30350504115896 86 20 20.9517220355126 -0.95172203551263 87 26 22.3458527538314 3.65414724616856 88 23 20.277603014333 2.72239698566701 89 27 23.8735000539034 3.12649994609657 90 17 21.7713595775746 -4.77135957757458 91 17 24.5393617922735 -7.53936179227349 92 19 19.7446276470909 -0.744627647090923 93 17 21.0574778252828 -4.05747782528279 94 22 22.3140787327237 -0.314078732723729 95 21 20.2748599287233 0.725140071276748 96 32 23.7531005562923 8.24689944370774 97 21 24.8963730185736 -3.89637301857364 98 21 23.5445900129273 -2.54459001292732 99 18 21.6976357594778 -3.69763575947777 100 18 22.5463624945708 -4.54636249457084 101 23 22.2529575680193 0.74704243198075 102 19 20.4749255888672 -1.47492558886723 103 20 22.5947108092125 -2.59471080921247 104 21 22.3374956245056 -1.33749562450561 105 20 23.3262373055206 -3.32623730552064 106 17 23.2864042242542 -6.28640422425423 107 18 20.9230769230769 -2.92307692307692 108 19 21.4843410760544 -2.48434107605441 109 22 22.9517220355126 -0.951722035512628 110 15 19.6552880468053 -4.65528804680529 111 14 19.7529847764168 -5.75298477641675 112 18 24.4910134776319 -6.49101347763185 113 24 21.6047405736656 2.39525942633442 114 35 23.7160105606356 11.2839894393644 115 29 19.0295173402673 9.97048265973272 116 21 21.6892786301519 -0.689278630151937 117 25 22.0298033168133 2.9701966831867 118 20 20.6935362468395 -0.693536246839551 119 22 22.2195109117843 -0.219510911784265 120 13 20.1879070873471 -7.18790708734707 121 26 24.2481560242200 1.75184397578003 122 17 18.6829625552748 -1.68296255527478 123 25 22.6699612510083 2.33003874899173 124 20 20.277603014333 -0.277603014332988 125 19 20.9565235793119 -1.95652357931191 126 21 22.0954680747514 -1.09546807475141 127 22 20.9741683233283 1.02583167667171 128 24 22.9857126188593 1.01428738114072 129 21 21.7056948196365 -0.705694819636464 130 26 25.8792722016689 0.120727798331079 131 24 21.2471854202538 2.75281457974624 132 16 18.8914730986397 -2.89147309863973 133 23 20.3588540580979 2.64114594190206 134 18 19.3311795496285 -1.33117954962845 135 16 22.3458527538314 -6.34585275383144 136 26 23.8427964832782 2.15720351672182 137 19 20.6324150821351 -1.63241508213507 138 21 21.4472510803977 -0.447251080397741 139 21 21.6223853176820 -0.622385317681966 140 22 23.6339296132130 -1.63392961321295 141 23 25.5949967857585 -2.59499678575849 142 29 24.9069467101384 4.09305328986159 143 21 21.8954024146074 -0.89540241460743 144 21 20.5120155845239 0.487984415476095 145 23 21.6552880468053 1.34471195319471 146 27 23.5445900129273 3.45540998707268 147 25 25.5869377255998 -0.586937725599795 148 21 21.8981455002172 -0.898145500217166 149 10 19.9841980877814 -9.9841980877814 150 20 22.7436850691051 -2.74368506910508 151 26 22.5947108092125 3.40528919078753 152 24 25.5785805962740 -1.57858059627396 153 29 28.1878647631732 0.812135236826824 154 19 22.3140787327237 -3.31407873272373 155 24 20.5989684259001 3.40103157409991 156 19 19.2155815958166 -0.215581595816563 157 24 23.9240475270431 0.075952472956865 158 22 21.2758305326895 0.724169467310533 159 17 25.5869377255998 -8.5869377255998 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.96623021520765 0.0675395695847 0.03376978479235 17 0.930967911833863 0.138064176332273 0.0690320881661366 18 0.943126595332907 0.113746809334187 0.0568734046670934 19 0.911161442861403 0.177677114277193 0.0888385571385967 20 0.881777107577307 0.236445784845386 0.118222892422693 21 0.85143950853018 0.297120982939641 0.148560491469820 22 0.816782713687218 0.366434572625565 0.183217286312782 23 0.750226540723055 0.499546918553891 0.249773459276945 24 0.698011675113096 0.603976649773809 0.301988324886904 25 0.62221925968065 0.755561480638701 0.377780740319351 26 0.58095010091082 0.838099798178361 0.419049899089180 27 0.521674992965865 0.95665001406827 0.478325007034135 28 0.510232149579109 0.979535700841782 0.489767850420891 29 0.446533467972737 0.893066935945474 0.553466532027263 30 0.419988583842592 0.839977167685184 0.580011416157408 31 0.353669275872203 0.707338551744407 0.646330724127796 32 0.311494381941058 0.622988763882117 0.688505618058942 33 0.463675543842037 0.927351087684075 0.536324456157963 34 0.737453047508821 0.525093904982358 0.262546952491179 35 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70 0.7085113920557 0.5829772158886 0.2914886079443 71 0.699629665021119 0.600740669957763 0.300370334978881 72 0.66047390964837 0.67905218070326 0.33952609035163 73 0.630092805631273 0.739814388737454 0.369907194368727 74 0.588083375999979 0.823833248000041 0.411916624000021 75 0.562960210960587 0.874079578078826 0.437039789039413 76 0.548647579510011 0.902704840979978 0.451352420489989 77 0.518073066840272 0.963853866319455 0.481926933159728 78 0.469533633268316 0.939067266536633 0.530466366731684 79 0.42377624413341 0.84755248826682 0.57622375586659 80 0.379172241904872 0.758344483809744 0.620827758095128 81 0.344408737425747 0.688817474851494 0.655591262574253 82 0.351341152528082 0.702682305056163 0.648658847471918 83 0.317436713091947 0.634873426183893 0.682563286908053 84 0.342058960868048 0.684117921736096 0.657941039131952 85 0.328751119159408 0.657502238318817 0.671248880840592 86 0.289794102211854 0.579588204423708 0.710205897788146 87 0.320066806925737 0.640133613851474 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0.472396661735125 0.94479332347025 0.527603338264875 106 0.552991469974734 0.894017060050532 0.447008530025266 107 0.542706899247044 0.914586201505913 0.457293100752956 108 0.504285195584127 0.991429608831747 0.495714804415873 109 0.462981537734121 0.925963075468242 0.537018462265879 110 0.484155497807546 0.968310995615093 0.515844502192454 111 0.494425791349429 0.988851582698858 0.505574208650571 112 0.590849207585634 0.818301584828733 0.409150792414366 113 0.638278149547836 0.723443700904328 0.361721850452164 114 0.940063189042126 0.119873621915747 0.0599368109578736 115 0.988005498604872 0.0239890027902566 0.0119945013951283 116 0.982264326410049 0.0354713471799022 0.0177356735899511 117 0.983462496950042 0.0330750060999151 0.0165375030499576 118 0.975607767644914 0.0487844647101728 0.0243922323550864 119 0.968015146785035 0.06396970642993 0.031984853214965 120 0.985666985969932 0.0286660280601352 0.0143330140300676 121 0.97863334937441 0.042733301251179 0.0213666506255895 122 0.970636769151523 0.0587264616969543 0.0293632308484771 123 0.992798009907925 0.0144039801841503 0.00720199009207515 124 0.98823457163062 0.0235308567387598 0.0117654283693799 125 0.987197310753243 0.0256053784935135 0.0128026892467568 126 0.979713805488178 0.040572389023644 0.020286194511822 127 0.96837482212067 0.0632503557586582 0.0316251778793291 128 0.963025712688952 0.0739485746220952 0.0369742873110476 129 0.956114586740964 0.0877708265180719 0.0438854132590359 130 0.939018853049577 0.121962293900846 0.0609811469504232 131 0.915160309763752 0.169679380472496 0.0848396902362482 132 0.890134568873956 0.219730862252088 0.109865431126044 133 0.885825647476292 0.228348705047416 0.114174352523708 134 0.83608474062885 0.327830518742300 0.163915259371150 135 0.794223257853069 0.411553484293863 0.205776742146931 136 0.722250613756782 0.555498772486436 0.277749386243218 137 0.82337481466202 0.353250370675959 0.176625185337980 138 0.783033272108509 0.433933455782982 0.216966727891491 139 0.725657476825725 0.54868504634855 0.274342523174275 140 0.626752400884829 0.746495198230342 0.373247599115171 141 0.501647666117784 0.996704667764431 0.498352333882216 142 0.550907406851049 0.898185186297902 0.449092593148951 143 0.493612995924665 0.98722599184933 0.506387004075335 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 10 0.078125 NOK 10% type I error level 16 0.125 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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Software written by Ed van Stee & Patrick Wessa

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