BEL20-MR3

*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, 22 Dec 2010 17:35:13 +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/Dec/22/t1293039231whoyx4fqio7lv52.htm/, Retrieved Wed, 22 Dec 2010 18:34:03 +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/Dec/22/t1293039231whoyx4fqio7lv52.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)

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Dataseries X:

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3.04 493 9 3.030 9.026 25.64 104.8 3.28 481 11 2.803 9.787 27.97 105.2 3.51 462 13 2.768 9.536 27.62 105.6 3.69 457 12 2.883 9.490 23.31 105.8 3.92 442 13 2.863 9.736 29.07 106.1 4.29 439 15 2.897 9.694 29.58 106.5 4.31 488 13 3.013 9.647 28.63 106.71 4.42 521 16 3.143 9.753 29.92 106.68 4.59 501 10 3.033 10.070 32.68 107.41 4.76 485 14 3.046 10.137 31.54 107.15 4.83 464 14 3.111 9.984 32.43 107.5 4.83 460 45 3.013 9.732 26.54 107.22 4.76 467 13 2.987 9.103 25.85 107.11 4.99 460 8 2.996 9.155 27.60 107.57 4.78 448 7 2.833 9.308 25.71 107.81 5.06 443 3 2.849 9.394 25.38 108.75 4.65 436 3 2.795 9.948 28.57 109.43 4.54 431 4 2.845 10.177 27.64 109.62 4.51 484 4 2.915 10.002 25.36 109.54 4.49 510 0 2.893 9.728 25.90 109.53 3.99 513 -4 2.604 10.002 26.29 109.84 3.97 503 -14 2.642 10.063 21.74 109.67 3.51 471 -18 2.660 10.018 19.20 109.79 3.34 471 -8 2.639 9.960 19.32 109.56 3.29 476 -1 2.720 10.236 19.82 110.22 3.28 475 1 2.746 10.893 20.36 110.4 3.26 470 2 2.736 10.756 24.31 110.69 3.32 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 9 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation BEL20[t] = + 25.3407584564391 + 0.65103168012121Eonia[t] + 0.0100078349950887Werkloosheid[t] + 0.0146904140268424Consumentenvertrouwen[t] + 0.0478695342530798Goudprijs[t] + 0.0140088218724249Olieprijs[t] -0.29522836259052CPI[t] + 0.12041578914747M1[t] + 0.214991787111754M2[t] + 0.253853974474647M3[t] + 0.41279744206612M4[t] + 0.508351332683476M5[t] + 0.353285138780970M6[t] -0.176219873416028M7[t] -0.354323152567325M8[t] -0.237604300450137M9[t] -0.150983928680366M10[t] + 0.0612238802489834M11[t] + 0.0582577172040336t + 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) 25.3407584564391 5.442797 4.6558 9e-06 4e-06 Eonia 0.65103168012121 0.056701 11.4818 0 0 Werkloosheid 0.0100078349950887 0.001479 6.7683 0 0 Consumentenvertrouwen 0.0146904140268424 0.005504 2.6692 0.008733 0.004366 Goudprijs 0.0478695342530798 0.018246 2.6236 0.009914 0.004957 Olieprijs 0.0140088218724249 0.00322 4.3507 3e-05 1.5e-05 CPI -0.29522836259052 0.050216 -5.8792 0 0 M1 0.12041578914747 0.143389 0.8398 0.402818 0.201409 M2 0.214991787111754 0.144354 1.4893 0.139208 0.069604 M3 0.253853974474647 0.144702 1.7543 0.08211 0.041055 M4 0.41279744206612 0.146344 2.8207 0.005669 0.002835 M5 0.508351332683476 0.149641 3.3971 0.000943 0.000472 M6 0.353285138780970 0.149806 2.3583 0.020093 0.010047 M7 -0.176219873416028 0.152243 -1.1575 0.249535 0.124767 M8 -0.354323152567325 0.15561 -2.277 0.024686 0.012343 M9 -0.237604300450137 0.151766 -1.5656 0.120265 0.060133 M10 -0.150983928680366 0.146112 -1.0333 0.303669 0.151834 M11 0.0612238802489834 0.14371 0.426 0.670908 0.335454 t 0.0582577172040336 0.009256 6.294 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.91674609555928 R-squared 0.840423403723184 Adjusted R-squared 0.814777165035838 F-TEST (value) 32.769850346042 F-TEST (DF numerator) 18 F-TEST (DF denominator) 112 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.32674486748741 Sum Squared Residuals 11.9573673440889 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3.03 2.41596885867001 0.614031141329988 2 2.803 2.58531491067328 0.217685089326717 3 2.768 2.53639437902601 0.231605620973994 4 2.883 2.68442598387716 0.198574016122839 5 2.863 2.85644597786120 0.0065540221387964 6 2.897 2.88691917955610 0.0100808204439044 7 3.013 2.81213939983851 0.200860600161486 8 3.143 3.17023952134700 -0.0272395213470044 9 3.033 3.00591457826105 0.0270854217389537 10 3.046 3.22410092517533 -0.178100925175330 11 3.111 3.23178801983939 -0.120788019839391 12 3.013 3.63228220971119 -0.619282209711191 13 2.987 3.35804419030867 -0.371044190308667 14 2.996 3.40830788407130 -0.412307884071296 15 2.833 3.14391926022497 -0.310919260224972 16 2.849 3.15658769226438 -0.307587692264380 17 2.795 2.84387404345814 -0.0488740434581386 18 2.845 2.58194385110813 0.263056148891867 19 2.915 2.60488184709506 0.310118152904940 20 2.893 2.67085850036198 0.222141499638018 21 2.604 2.41863997901302 0.185360020986977 22 2.642 2.29288366787538 0.349116332124615 23 2.66 1.81169830509459 0.848301694905411 24 2.639 1.91176804573129 0.727231954268709 25 2.72 2.03612772432040 0.683872275679604 26 2.746 2.18769805829518 0.558301941704816 27 2.736 2.19960906336324 0.536390936636757 28 2.812 2.35962659368962 0.452373406310384 29 2.799 2.40906785470407 0.389932145295934 30 2.555 2.40383274519865 0.151167254801353 31 2.305 2.37078553032195 -0.0657855303219517 32 2.215 2.31193015597949 -0.0969301559794918 33 2.066 2.43981397096282 -0.373813970962824 34 1.94 2.55133029841349 -0.611330298413494 35 2.042 2.68731638396233 -0.645316383962329 36 1.995 2.55598934647724 -0.560989346477236 37 1.947 2.50326904845860 -0.556269048458596 38 1.766 2.35634482832840 -0.590344828328395 39 1.635 2.14737069723765 -0.512370697237647 40 1.833 2.32477946775227 -0.491779467752267 41 1.91 2.53935271906632 -0.629352719066317 42 1.96 2.18452727212114 -0.224527272121142 43 1.97 2.15815409084331 -0.188154090843305 44 2.061 2.14239716984115 -0.0813971698411475 45 2.093 2.22188748651441 -0.128887486514414 46 2.121 2.20159503989760 -0.0805950398976033 47 2.175 2.34578059815185 -0.170780598151848 48 2.197 2.51151340570407 -0.314513405704073 49 2.35 2.69199216736911 -0.341992167369114 50 2.44 2.73112330932109 -0.291123309321093 51 2.409 2.72190479022424 -0.312904790224244 52 2.473 2.69844195946684 -0.225441959466841 53 2.408 2.61147742036940 -0.203477420369403 54 2.455 2.66602079543612 -0.211020795436117 55 2.448 2.59057086886206 -0.142570868862062 56 2.498 2.67351783357509 -0.175517833575091 57 2.646 2.87059351794612 -0.224593517946122 58 2.757 2.90455916339446 -0.147559163394463 59 2.849 2.95825971345761 -0.109259713457614 60 2.921 2.99281480703369 -0.0718148070336882 61 2.982 3.10225466502076 -0.120254665020761 62 3.081 3.0612201812079 0.0197798187921011 63 3.106 3.01150834826823 0.094491651731769 64 3.119 3.0104882811983 0.108511718801699 65 3.061 2.94163907043094 0.119360929569062 66 3.097 2.83224767974109 0.264752320258914 67 3.162 2.71313304001469 0.448866959985308 68 3.257 2.7402356909808 0.516764309019201 69 3.277 2.84653714375659 0.430462856243405 70 3.295 2.9358416219813 0.359158378018701 71 3.364 3.02279067053168 0.341209329468319 72 3.494 3.28379999474493 0.210200005255074 73 3.667 3.62834514647274 0.0386548535272572 74 3.813 3.58913636985116 0.223863630148842 75 3.918 3.68064854190366 0.237351458096342 76 3.896 3.87886578317594 0.0171342168240591 77 3.801 3.93251218175961 -0.131512181759608 78 3.57 3.8557929792219 -0.285792979221896 79 3.702 3.91298582428604 -0.210985824286041 80 3.862 3.90184690926293 -0.0398469092629297 81 3.97 3.98777290376445 -0.0177729037644518 82 4.139 4.03347935802828 0.105520641971716 83 4.2 4.03802142733868 0.161978572661320 84 4.291 3.90265761538329 0.388342384616713 85 4.444 4.15415400017194 0.289845999828062 86 4.503 4.11097490496396 0.392025095036035 87 4.357 4.11379868246633 0.243201317533671 88 4.591 4.32918546451529 0.261814535484708 89 4.697 4.34685164503624 0.350148354963755 90 4.621 4.25486674124944 0.366133258750559 91 4.563 4.29004907930392 0.272950920696079 92 4.203 4.24411207986393 -0.0411120798639304 93 4.296 4.24847759278414 0.0475224072158632 94 4.435 4.11377126795341 0.321228732046587 95 4.105 4.00818205934774 0.0968179406522602 96 4.117 3.88386249370042 0.233137506299584 97 3.844 4.10848017254059 -0.264480172540592 98 3.721 4.01826944281291 -0.297269442812913 99 3.674 3.85268559859748 -0.178685598597476 100 3.858 3.85352957698012 0.00447042301988056 101 3.801 3.63913781366492 0.161862186335084 102 3.504 3.51204963069439 -0.0080496306943907 103 3.033 3.50962452096158 -0.476624520961581 104 3.047 3.45786021001849 -0.410860210018485 105 2.962 3.23467339233154 -0.272673392331541 106 2.198 2.61256231185604 -0.414562311856042 107 2.014 2.25550697759211 -0.241506977592114 108 1.863 1.85971989365845 0.00328010634154698 109 1.905 1.82821745547459 0.0767825445254115 110 1.811 1.59878875611755 0.212211243882449 111 1.67 1.83130727632127 -0.161307276321270 112 1.864 1.86149268019524 0.00250731980475932 113 2.052 2.02003146159395 0.0319685384060461 114 2.03 2.22645019677436 -0.19645019677436 115 2.071 2.01564954280566 0.0553504571943378 116 2.293 2.06810052442989 0.22489947557011 117 2.443 2.15241174227281 0.290588257727189 118 2.513 2.19261728592130 0.320382714078697 119 2.467 2.47205418208476 -0.00505418208475503 120 2.503 2.49859218785544 0.00440781214455776 121 2.54 2.58914657119259 -0.0491465711925924 122 2.483 2.51582135435726 -0.0328213543572620 123 2.626 2.49285336236692 0.133146637633076 124 2.656 2.67657651688484 -0.0205765168848406 125 2.447 2.49360981205521 -0.046609812055209 126 2.467 2.59634892889869 -0.129348928898690 127 2.462 2.66602625566721 -0.204026255667208 128 2.505 2.59590140433925 -0.0909014043392488 129 2.579 2.54227769239303 0.0367223076069659 130 2.649 2.67225905950338 -0.0232590595033843 131 2.637 2.79260166259926 -0.155601662599259 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 22 0.0365539644196985 0.073107928839397 0.963446035580302 23 0.0122647074281565 0.0245294148563130 0.987735292571843 24 0.00487314470258516 0.00974628940517031 0.995126855297415 25 0.00231007340371228 0.00462014680742457 0.997689926596288 26 0.00196304808423506 0.00392609616847011 0.998036951915765 27 0.00113776282889521 0.00227552565779042 0.998862237171105 28 0.000616312423512185 0.00123262484702437 0.999383687576488 29 0.000423971919227570 0.000847943838455139 0.999576028080772 30 0.00108457753817057 0.00216915507634114 0.99891542246183 31 0.00428077201300573 0.00856154402601147 0.995719227986994 32 0.00722926702022238 0.0144585340404448 0.992770732979778 33 0.00591948783744681 0.0118389756748936 0.994080512162553 34 0.00395139188168583 0.00790278376337166 0.996048608118314 35 0.00563272456383847 0.0112654491276769 0.994367275436162 36 0.0134467186121162 0.0268934372242323 0.986553281387884 37 0.0258077369265930 0.0516154738531861 0.974192263073407 38 0.0162570497308345 0.0325140994616690 0.983742950269165 39 0.0106239420298770 0.0212478840597540 0.989376057970123 40 0.0234946037558891 0.0469892075117782 0.97650539624411 41 0.0369314772473947 0.0738629544947893 0.963068522752605 42 0.116433215288860 0.232866430577719 0.88356678471114 43 0.177252237747905 0.354504475495810 0.822747762252095 44 0.221086491297939 0.442172982595878 0.778913508702061 45 0.222949861607342 0.445899723214683 0.777050138392658 46 0.446779393103134 0.893558786206268 0.553220606896866 47 0.493778786990356 0.987557573980713 0.506221213009644 48 0.585071567791086 0.829856864417827 0.414928432208914 49 0.623708475273728 0.752583049452544 0.376291524726272 50 0.726443963544381 0.547112072911238 0.273556036455619 51 0.772009514389882 0.455980971220236 0.227990485610118 52 0.785497329396633 0.429005341206735 0.214502670603367 53 0.823118027381692 0.353763945236617 0.176881972618308 54 0.808251554621078 0.383496890757845 0.191748445378922 55 0.788729454207095 0.422541091585809 0.211270545792905 56 0.778685748391431 0.442628503217138 0.221314251608569 57 0.85984721949027 0.28030556101946 0.14015278050973 58 0.868867951953038 0.262264096093924 0.131132048046962 59 0.87680686147604 0.246386277047921 0.123193138523960 60 0.94734413574822 0.105311728503560 0.0526558642517802 61 0.978859936330559 0.0422801273388826 0.0211400636694413 62 0.991069688717417 0.0178606225651655 0.00893031128258276 63 0.992780180734461 0.0144396385310783 0.00721981926553913 64 0.995287620410357 0.00942475917928544 0.00471237958964272 65 0.996692886533392 0.00661422693321648 0.00330711346660824 66 0.99509604914498 0.00980790171004045 0.00490395085502023 67 0.99408979326041 0.0118204134791790 0.00591020673958952 68 0.994763207743684 0.0104735845126316 0.00523679225631578 69 0.997431436992287 0.00513712601542501 0.00256856300771250 70 0.997435812239229 0.00512837552154222 0.00256418776077111 71 0.998268706527056 0.00346258694588877 0.00173129347294439 72 0.997291044007429 0.0054179119851425 0.00270895599257125 73 0.996689767597989 0.00662046480402285 0.00331023240201143 74 0.995307689543856 0.0093846209122871 0.00469231045614355 75 0.995659973597485 0.00868005280503084 0.00434002640251542 76 0.993460488702633 0.0130790225947335 0.00653951129736676 77 0.993494196064906 0.0130116078701874 0.00650580393509368 78 0.997437241116748 0.00512551776650359 0.00256275888325179 79 0.99820921350534 0.00358157298932179 0.00179078649466090 80 0.997125993752668 0.00574801249466347 0.00287400624733173 81 0.997750951600282 0.00449809679943697 0.00224904839971848 82 0.999311252482675 0.00137749503464989 0.000688747517324943 83 0.999507882109164 0.000984235781672534 0.000492117890836267 84 0.999539473880244 0.000921052239512349 0.000460526119756174 85 0.9994416450775 0.00111670984500034 0.000558354922500168 86 0.999147567159988 0.00170486568002486 0.000852432840012428 87 0.998606437579412 0.00278712484117607 0.00139356242058803 88 0.997763927733039 0.00447214453392191 0.00223607226696096 89 0.997479116868416 0.00504176626316756 0.00252088313158378 90 0.996204392414757 0.00759121517048524 0.00379560758524262 91 0.996726555569583 0.00654688886083429 0.00327344443041714 92 0.994462430450818 0.0110751390983647 0.00553756954918235 93 0.990882890434634 0.0182342191307314 0.00911710956536568 94 0.99201840922019 0.0159631815596191 0.00798159077980953 95 0.998666306050297 0.00266738789940665 0.00133369394970333 96 0.99924679135916 0.00150641728168003 0.000753208640840016 97 0.999419979266056 0.00116004146788834 0.000580020733944168 98 0.999592492959544 0.000815014080911218 0.000407507040455609 99 0.999661915132047 0.000676169735905686 0.000338084867952843 100 0.999255355881237 0.00148928823752641 0.000744644118763206 101 0.999336909515234 0.00132618096953215 0.000663090484766076 102 0.999762563681794 0.000474872636411551 0.000237436318205775 103 0.99946079636405 0.00107840727189717 0.000539203635948587 104 0.998498710336058 0.00300257932788452 0.00150128966394226 105 0.996544598452075 0.00691080309584922 0.00345540154792461 106 0.995786196820514 0.00842760635897167 0.00421380317948584 107 0.987347989989634 0.0253040200207326 0.0126520100103663 108 0.989866357925545 0.0202672841489099 0.0101336420744550 109 0.977481155598716 0.0450376888025689 0.0225188444012844 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 45 0.511363636363636 NOK 5% type I error level 66 0.75 NOK 10% type I error level 69 0.784090909090909 NOK

Charts produced by software:

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Parameters (Session):

par1 = 4 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 4 ; 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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Software written by Ed van Stee & Patrick Wessa

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