Time Series Analysis Multiple Lineair Regression (trend, seizoenaliteit, verleden)

*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: Fri, 17 Dec 2010 12:22:00 +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/17/t1292588470oh8co3i7uu8qmqv.htm/, Retrieved Fri, 17 Dec 2010 13:21:22 +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/17/t1292588470oh8co3i7uu8qmqv.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 «

40399 44164 44496 43110 43880 36763 40399 44164 44496 43110 37903 36763 40399 44164 44496 35532 37903 36763 40399 44164 35533 35532 37903 36763 40399 32110 35533 35532 37903 36763 33374 32110 35533 35532 37903 35462 33374 32110 35533 35532 33508 35462 33374 32110 35533 36080 33508 35462 33374 32110 34560 36080 33508 35462 33374 38737 34560 36080 33508 35462 38144 38737 34560 36080 33508 37594 38144 38737 34560 36080 36424 37594 38144 38737 34560 36843 36424 37594 38144 38737 37246 36843 36424 37594 38144 38661 37246 36843 36424 37594 40454 38661 37246 36843 36424 44928 40454 38661 37246 36843 48441 44928 40454 38661 37246 48140 48441 44928 40454 38661 45998 48140 48441 44928 40454 47369 45998 48140 48441 44928 49554 47369 45998 48140 48441 47510 49554 47369 45998 48140 44873 47510 49554 47369 45998 45344 44873 47510 49554 47369 42413 45344 44873 47510 49554 36912 42413 45344 44873 47510 43452 36912 42413 45344 44873 42142 43452 36912 42413 45344 44382 42142 43452 36912 42413 4 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 11 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation OPENVAC[t] = + 2250.36966939532 + 0.921167547866439Y1[t] + 0.147604467143337Y2[t] -0.0352136681864466Y3[t] -0.0684979108690105Y4[t] -1782.24140296891M1[t] -2244.99351674281M2[t] -1446.45969762698M3[t] -2768.68887159723M4[t] -3252.36709616257M5[t] -2662.16780990246M6[t] + 1003.87028803039M7[t] + 778.856112992519M8[t] + 901.696972060029M9[t] + 881.942687673479M10[t] -1391.42130378499M11[t] -2.68490575599361t + 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) 2250.36966939532 1252.69288 1.7964 0.075222 0.037611 Y1 0.921167547866439 0.095886 9.6069 0 0 Y2 0.147604467143337 0.130041 1.1351 0.258863 0.129431 Y3 -0.0352136681864466 0.12807 -0.275 0.783875 0.391938 Y4 -0.0684979108690105 0.095391 -0.7181 0.474262 0.237131 M1 -1782.24140296891 733.338533 -2.4303 0.016733 0.008367 M2 -2244.99351674281 729.357054 -3.078 0.002641 0.001321 M3 -1446.45969762698 758.801315 -1.9062 0.059277 0.029638 M4 -2768.68887159723 813.506152 -3.4034 0.000935 0.000467 M5 -3252.36709616257 812.052227 -4.0051 0.000114 5.7e-05 M6 -2662.16780990246 862.123173 -3.0879 0.002562 0.001281 M7 1003.87028803039 918.587877 1.0928 0.276895 0.138448 M8 778.856112992519 927.154085 0.8401 0.402736 0.201368 M9 901.696972060029 812.294747 1.1101 0.269438 0.134719 M10 881.942687673479 743.516388 1.1862 0.238155 0.119078 M11 -1391.42130378499 743.95932 -1.8703 0.064151 0.032075 t -2.68490575599361 5.368187 -0.5002 0.617986 0.308993 Multiple Linear Regression - Regression Statistics Multiple R 0.982023446627715 R-squared 0.964370049726577 Adjusted R-squared 0.959091538574959 F-TEST (value) 182.697359544449 F-TEST (DF numerator) 16 F-TEST (DF denominator) 108 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1581.2481549845 Sum Squared Residuals 270037338.585324 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 40399 43191.945750204 -2792.94575020402 2 36763 39213.245477128 -2450.24547712803 3 37903 36020.7512010242 1882.24879897577 4 35532 35364.5990504630 167.400949536961 5 35533 33248.3482886415 2284.65171135847 6 32110 33695.7284672838 -1585.72846728383 7 33374 34211.4767364604 -837.4767364604 8 35462 34805.2566781403 656.743321859688 9 33508 37155.8504061575 -3647.85040615747 10 36080 35831.6062271961 248.393772803855 11 34560 35476.2736357844 -916.273635784363 12 38737 35770.2579202909 2966.74207970915 13 38144 37651.9650322087 492.034967791303 14 37594 37134.16766494 459.832335059972 15 36424 37292.8743104634 -868.874310463432 16 36843 34543.7776743393 2299.22232566067 17 37246 34330.6732986642 2915.32670133581 18 38661 35430.1383154476 3230.86168455236 19 40454 40521.8162168609 -67.8162168608912 20 44928 42111.7391374661 2816.26086253387 21 48441 46540.4215109561 1900.57848904394 22 48140 50254.3636515297 -2114.36365152966 23 45998 47939.2151098277 -1941.21510982765 24 47369 46880.2164061496 488.783593850354 25 49554 45812.0081901699 3741.99180983011 26 47510 47657.7335356086 -147.733535608623 27 44873 46991.6763278354 -2118.67632783545 28 45344 42765.0873927556 2578.91260724437 29 42413 42245.6700001467 167.329999853287 30 36912 40435.6321747027 -3523.63217470272 31 43452 38763.0573461149 4688.94265388506 32 42142 43818.7706000473 -1676.77060004726 33 44382 44092.0080462219 289.991953778116 34 43636 46086.1319290935 -2450.13192909346 35 44167 43051.6796158126 1115.32038418737 36 44423 44830.2966957705 -407.296695770532 37 42868 43232.4013274731 -364.401327473056 38 43908 41404.7364983008 2503.26350169918 39 42013 42883.6876253067 -870.687625306688 40 38846 40003.8914770501 -1157.89147705007 41 35087 36390.3722938865 -1303.37229388651 42 33026 33043.2465884273 -17.2465884272728 43 34646 34494.5535007028 151.446499297158 44 37135 35804.2341031052 1330.76589689479 45 37985 38786.3543369174 -801.354336917373 46 43121 39998.4231330201 3122.57686697995 47 43722 42380.3411229956 1341.65887700440 48 43630 44880.3728424291 -1250.37284242906 49 42234 42860.3287800093 -626.328780009342 50 39351 40722.3935678774 -1371.39356787742 51 39327 38618.5330176471 708.466982352912 52 35704 36901.427326586 -1197.42732658604 53 30466 33271.2757520878 -2805.27575208782 54 28155 28697.2681374790 -542.268137479045 55 29257 29587.8739973401 -330.873997340107 56 29998 30466.8047557658 -468.804755765845 57 32529 31872.3768291491 656.623170850894 58 34787 34410.2808224866 376.719177513448 59 33855 34486.2371287905 -631.237128790494 60 34556 35209.8535128838 -653.853512883788 61 31348 33680.2176166612 -2332.21761666121 62 30805 30241.2966910508 563.703308949204 63 28353 30102.5917668545 -1749.59176685455 64 24514 26503.7740461239 -1989.77404612391 65 21106 22357.9848660009 -1251.98486600087 66 21346 19363.0449740079 1982.95502599207 67 23335 23047.5845032668 287.415496733181 68 24379 25070.4844082993 -691.484408299252 69 26290 26670.9141666083 -380.91416660827 70 30084 28476.4457395047 1607.55426049527 71 29429 29804.3732413013 -375.37324130135 72 30632 31010.9511049681 -378.951104968099 73 27349 29973.0082655776 -2624.00826557758 74 27264 26424.1302392007 839.869760799304 75 27474 26659.5985341512 814.401465848787 76 24482 25548.7867456504 -1066.78674565042 77 21453 22565.1590533916 -1112.15905339161 78 18788 19919.2518178201 -1131.25181782013 79 19282 20771.5742978871 -1489.57429788711 80 19713 20917.1740310591 -1204.17403105911 81 21917 21808.594402009 108.405597991021 82 23812 24045.1773930847 -233.177393084652 83 23785 23791.0461857034 -6.04618570336439 84 24696 25327.4880009091 -631.488000909115 85 24562 24160.0607109091 401.939289090947 86 23580 23576.8021374769 3.19786252310590 87 24939 23418.0553121103 1520.94468788971 88 23899 23142.3773779351 756.622622064904 89 21454 21942.353010896 -488.353010895997 90 19761 20143.5136644456 -382.513664445586 91 19815 21829.9708299620 -2014.97082996202 92 20780 21559.4556798989 -779.455679898907 93 23462 22803.6030904417 658.396909558336 94 25005 25508.2389994894 -503.238999489397 95 24725 25011.7467325244 -286.746732524439 96 26198 26209.7663678884 -11.7663678883515 97 27543 25502.3445194082 2040.65548059179 98 26471 26397.4667824821 73.5332175178714 99 26558 26371.6617746416 186.338225358382 100 25317 24820.3974763813 496.602523618725 101 22896 23149.3263699762 -253.326369976211 102 22248 21393.8831446902 854.116855309849 103 23406 24140.7101948693 -734.71019486931 104 25073 25054.3336378637 18.6663621362638 105 27691 27069.6537656193 621.346234380717 106 30599 29708.4970810022 890.502918997767 107 31948 30359.6101423115 1588.38985768848 108 32946 33213.8599521344 -267.859952134403 109 34012 32265.6484046153 1746.35159538469 110 32936 32682.7900861295 253.209913870508 111 32974 32517.2621573475 456.737842652531 112 30951 30962.6313524599 -11.6313524598961 113 29812 28583.2263765385 1228.77362346155 114 29010 27895.2927156957 1114.70728430431 115 31068 30720.3823765356 347.61762346444 116 32447 32448.7469683542 -1.74696835424703 117 34844 34249.2234459199 594.776554080096 118 35676 36620.8350235931 -944.835023593117 119 35387 35275.4770849486 111.522915051416 120 36488 36341.9371965762 146.062803423847 121 35652 35335.0714027636 316.928597236367 122 33488 34215.2373198051 -727.237319805074 123 32914 32875.307972618 38.6920273820277 124 29781 30656.2500802553 -875.250080255301 125 27951 27332.6106897701 618.3893102299 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.385204799851253 0.770409599702506 0.614795200148747 21 0.851225311715645 0.29754937656871 0.148774688284355 22 0.926500072001927 0.146999855996146 0.073499927998073 23 0.940498180643078 0.119003638713844 0.0595018193569219 24 0.969887297932574 0.0602254041348515 0.0301127020674258 25 0.96900625987987 0.0619874802402586 0.0309937401201293 26 0.967503989598452 0.0649920208030962 0.0324960104015481 27 0.992436951501128 0.0151260969977433 0.00756304849887164 28 0.991225756809333 0.0175484863813335 0.00877424319066673 29 0.997591801300552 0.00481639739889507 0.00240819869944754 30 0.999907840703867 0.000184318592265376 9.2159296132688e-05 31 0.999997961622033 4.07675593405603e-06 2.03837796702801e-06 32 0.999999201569368 1.59686126486285e-06 7.98430632431423e-07 33 0.999998175245544 3.64950891174452e-06 1.82475445587226e-06 34 0.999999351983103 1.29603379487977e-06 6.48016897439887e-07 35 0.999998990080976 2.01983804891930e-06 1.00991902445965e-06 36 0.9999990621595 1.87568099831732e-06 9.37840499158658e-07 37 0.99999914071709 1.71856582108583e-06 8.59282910542915e-07 38 0.999999683158712 6.33682575389931e-07 3.16841287694966e-07 39 0.99999950121033 9.97579338842651e-07 4.98789669421326e-07 40 0.99999982113977 3.57720458603036e-07 1.78860229301518e-07 41 0.999999944786376 1.10427248114406e-07 5.52136240572031e-08 42 0.999999873128208 2.53743583224956e-07 1.26871791612478e-07 43 0.999999827949426 3.4410114869186e-07 1.7205057434593e-07 44 0.999999853944578 2.92110844676388e-07 1.46055422338194e-07 45 0.999999752631394 4.94737211531031e-07 2.47368605765516e-07 46 0.999999993214097 1.35718066136823e-08 6.78590330684113e-09 47 0.999999994620702 1.07585962471232e-08 5.37929812356159e-09 48 0.999999994110197 1.17796053158051e-08 5.88980265790255e-09 49 0.999999991398961 1.72020777302895e-08 8.60103886514477e-09 50 0.999999985731774 2.8536451970047e-08 1.42682259850235e-08 51 0.999999983022465 3.39550697183265e-08 1.69775348591633e-08 52 0.999999976633691 4.67326173996472e-08 2.33663086998236e-08 53 0.999999995467071 9.06585728423044e-09 4.53292864211522e-09 54 0.999999989173584 2.16528327544464e-08 1.08264163772232e-08 55 0.999999985581314 2.88373713889153e-08 1.44186856944576e-08 56 0.9999999715014 5.69972012206241e-08 2.84986006103121e-08 57 0.99999995336087 9.32782584453115e-08 4.66391292226557e-08 58 0.999999929072731 1.41854537231043e-07 7.09272686155216e-08 59 0.99999984389663 3.12206738130407e-07 1.56103369065204e-07 60 0.999999735509784 5.28980430968667e-07 2.64490215484334e-07 61 0.999999800294723 3.99410554321873e-07 1.99705277160936e-07 62 0.999999809334827 3.81330345136359e-07 1.90665172568179e-07 63 0.999999823241028 3.53517943953992e-07 1.76758971976996e-07 64 0.99999982576346 3.48473077983412e-07 1.74236538991706e-07 65 0.999999640650287 7.18699425100235e-07 3.59349712550117e-07 66 0.999999879525101 2.40949798295866e-07 1.20474899147933e-07 67 0.999999868228691 2.63542617196725e-07 1.31771308598363e-07 68 0.999999702195095 5.95609809569991e-07 2.97804904784996e-07 69 0.999999333830248 1.33233950305508e-06 6.66169751527541e-07 70 0.999999931382903 1.37234194177596e-07 6.86170970887978e-08 71 0.999999831974763 3.36050473354638e-07 1.68025236677319e-07 72 0.999999686941485 6.2611702926028e-07 3.1305851463014e-07 73 0.999999982484084 3.50318320843567e-08 1.75159160421783e-08 74 0.999999999711027 5.77945377908789e-10 2.88972688954394e-10 75 0.999999999352597 1.29480517919056e-09 6.4740258959528e-10 76 0.999999998118803 3.76239460874773e-09 1.88119730437386e-09 77 0.99999999446584 1.10683197889301e-08 5.53415989446507e-09 78 0.999999987736055 2.45278889018674e-08 1.22639444509337e-08 79 0.999999973797255 5.24054897947432e-08 2.62027448973716e-08 80 0.999999932961754 1.34076492492214e-07 6.7038246246107e-08 81 0.999999823592898 3.52814204479853e-07 1.76407102239926e-07 82 0.999999576478867 8.4704226698811e-07 4.23521133494055e-07 83 0.999998889901608 2.22019678473166e-06 1.11009839236583e-06 84 0.999997106124435 5.78775112923357e-06 2.89387556461679e-06 85 0.999994352182856 1.12956342888460e-05 5.64781714442301e-06 86 0.999988884292922 2.22314141569687e-05 1.11157070784843e-05 87 0.999993448680009 1.31026399821181e-05 6.55131999105904e-06 88 0.999991281339698 1.74373206043069e-05 8.71866030215347e-06 89 0.999984183443482 3.16331130351706e-05 1.58165565175853e-05 90 0.99997526781381 4.94643723777128e-05 2.47321861888564e-05 91 0.999980490080907 3.90198381866265e-05 1.95099190933132e-05 92 0.999947840371039 0.000104319257922885 5.21596289614426e-05 93 0.999886220743953 0.000227558512093389 0.000113779256046694 94 0.999700689592857 0.000598620814285722 0.000299310407142861 95 0.999608171195381 0.000783657609237939 0.000391828804618969 96 0.998997193665068 0.00200561266986315 0.00100280633493157 97 0.998687037287753 0.00262592542449377 0.00131296271224689 98 0.99678561969262 0.00642876061475802 0.00321438030737901 99 0.993273427960491 0.0134531440790175 0.00672657203950876 100 0.985993789080785 0.0280124218384297 0.0140062109192149 101 0.989820371888146 0.0203592562237079 0.0101796281118539 102 0.976958205919829 0.046083588160343 0.0230417940801715 103 0.981458063333466 0.0370838733330684 0.0185419366665342 104 0.956664008517439 0.0866719829651226 0.0433359914825613 105 0.991884407286382 0.0162311854272357 0.00811559271361785 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 70 0.813953488372093 NOK 5% type I error level 78 0.906976744186046 NOK 10% type I error level 82 0.953488372093023 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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