Multiple Regression: BEL20

*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, 15 Dec 2010 16:09:52 +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/15/t1292429395fo4agtp8ib2rfsa.htm/, Retrieved Wed, 15 Dec 2010 17:09:55 +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/15/t1292429395fo4agtp8ib2rfsa.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:

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3.32 523 -3 2065.81 10457 28.37 111.22 3.30 519 -3 1940.49 10368 27.34 111.09 3.30 509 -4 2042.00 10244 24.46 111 3.09 512 -8 1995.37 10511 27.46 111.06 2.79 519 -9 1946.81 10812 30.23 111.55 2.76 517 -13 1765.90 10738 32.33 112.32 2.75 510 -18 1635.25 10171 29.87 112.64 2.56 509 -11 1833.42 9721 24.87 112.36 2.56 501 -9 1910.43 9897 25.48 112.04 2.21 507 -10 1959.67 9828 27.28 112.37 2.08 569 -13 1969.60 9924 28.24 112.59 2.10 580 -11 2061.41 10371 29.58 112.89 2.02 578 -5 2093.48 10846 26.95 113.22 2.01 565 -15 2120.88 10413 29.08 112.85 1.97 547 -6 2174.56 10709 28.76 113.06 2.06 555 -6 2196.72 10662 29.59 112.99 2.02 562 -3 2350.44 10570 30.70 113.32 2.03 561 -1 2440.25 10297 30.52 113.74 2.01 555 -3 2408.64 10635 32.67 113.91 2.08 544 -4 2472.81 10872 33.19 114.52 2.02 537 -6 2407.60 10296 37.13 114.96 2.03 543 0 2454.62 10383 35.54 114.91 2.07 594 -4 2448.05 10431 37.75 115.3 2.04 611 -2 2497.84 10574 41.84 115.44 2.05 613 -2 2645.64 10653 42.94 115.52 2.11 611 -6 2756.76 10805 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation BEL20[t] = -11758.0439129434 + 367.515883266902Eonia[t] + 6.35912382598829Werkloosheid[t] + 68.391966647122Consumentenvertrouwen[t] -0.0455205559904629Goudprijs[t] + 2.57032745934439Olieprijs[t] + 94.2348952432752CPI[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) -11758.0439129434 2991.788773 -3.9301 0.000164 8.2e-05 Eonia 367.515883266902 73.717042 4.9855 3e-06 1e-06 Werkloosheid 6.35912382598829 1.607454 3.956 0.00015 7.5e-05 Consumentenvertrouwen 68.391966647122 10.026709 6.821 0 0 Goudprijs -0.0455205559904629 0.026444 -1.7214 0.088541 0.044271 Olieprijs 2.57032745934439 4.086512 0.629 0.530922 0.265461 CPI 94.2348952432752 30.181142 3.1223 0.002399 0.001199 Multiple Linear Regression - Regression Statistics Multiple R 0.881398336649735 R-squared 0.77686302784892 Adjusted R-squared 0.762310616621675 F-TEST (value) 53.3838011940256 F-TEST (DF numerator) 6 F-TEST (DF denominator) 92 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 421.280954316182 Sum Squared Residuals 16327943.1071989 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2065.81 2660.47146553964 -594.661465539643 2 1940.49 2616.83800838873 -676.348008388732 3 2042 2474.61566876974 -432.615668769738 4 1995.37 2144.15792581634 -148.787925816342 5 1946.81 2049.61827934953 -102.808279349527 6 1765.9 1833.63376675629 -67.7337667562932 7 1635.25 1493.12722408055 142.122775919449 8 1833.42 1876.93069119457 -43.5106911945742 9 1910.43 1926.24274929894 -15.8127492989429 10 1959.67 1806.23998968478 153.430010315222 11 1969.6 1966.38192006939 3.218079930605 12 2061.41 2191.83355195561 -130.423551955615 13 2093.48 2562.78112364175 -469.301123641753 14 2120.88 1782.83597559228 338.044024407722 15 2174.56 2274.69154985883 -100.131549858832 16 2196.72 2356.31736521654 -159.597365216538 17 2350.44 2629.44496667042 -279.004966670421 18 2440.25 2815.08804381624 -374.838043816236 19 2408.64 2638.95923820489 -230.319238204895 20 2472.81 2574.3745059081 -101.564505908102 21 2407.6 2448.83603718329 -41.2360371832907 22 2454.62 2888.25888506093 -433.63888506093 23 2448.05 2993.95401507101 -545.904015071007 24 2497.84 3245.01366204519 -747.173662045185 25 2645.64 3268.17709643133 -622.537096431325 26 2756.76 3065.73038226049 -308.970382260494 27 2849.27 2853.99664692998 -4.72664692998338 28 2921.44 2878.19768166310 43.2423183369043 29 2981.85 2914.17232365289 67.6776763471108 30 3080.58 3174.39765591537 -93.8176559153676 31 3106.22 3296.79132892675 -190.571328926755 32 3119.31 3050.05351694628 69.2564830537221 33 3061.26 2607.58991824019 453.670081759807 34 3097.31 2629.91983350499 467.390166495006 35 3161.69 3037.94637272667 123.743627273332 36 3257.16 3174.92986512176 82.230134878243 37 3277.01 2895.38082909896 381.629170901037 38 3295.32 3136.26220113067 159.057798869329 39 3363.99 2935.69741396033 428.292586039667 40 3494.17 3257.71671958524 236.453280414761 41 3667.03 3508.46653345497 158.563466545033 42 3813.06 3455.080910652 357.979089348002 43 3917.96 3250.41192751311 667.548072486889 44 3895.51 3338.08322841624 557.426771583756 45 3801.06 3180.65957191672 620.400428083284 46 3570.12 3576.89127414298 -6.77127414297906 47 3701.61 3926.66174375049 -225.051743750486 48 3862.27 4047.15347343285 -184.883473432853 49 3970.1 4008.70580416341 -38.605804163412 50 4138.52 4140.93597963593 -2.41597963592681 51 4199.75 3946.181792495 253.568207505003 52 4290.89 3374.45601436162 916.433985638376 53 4443.91 3874.69404771795 569.215952282053 54 4502.64 3974.51545785273 528.124542147269 55 4356.98 3802.99108028159 553.988919718405 56 4591.27 4073.28497571189 517.985024288109 57 4696.96 3963.46632746312 733.493672536883 58 4621.4 3897.23108859242 724.168911407578 59 4562.84 4220.47759376793 342.362406232072 60 4202.52 4183.99649188368 18.5235081163186 61 4296.49 4019.03015602455 277.459843975451 62 4435.23 4005.98121039268 429.248789607321 63 4105.18 3554.66895586674 550.511044133264 64 4116.68 3827.83214527474 288.847854725265 65 3844.49 3737.82149166792 106.668508332082 66 3720.98 3944.71015158458 -223.730151584578 67 3674.4 3982.2530219505 -307.853021950496 68 3857.62 3767.19008887847 90.4299111215299 69 3801.06 3655.16480840234 145.895191597659 70 3504.37 3667.00342221297 -162.633422212973 71 3032.6 3950.60301208027 -918.003012080274 72 3047.03 4089.86934628125 -1042.83934628125 73 2962.34 4077.62554796322 -1115.28554796322 74 2197.82 3148.24307756284 -950.423077562838 75 2014.45 2430.43983648512 -415.989836485122 76 1862.83 1994.61213878252 -131.782138782517 77 1905.41 2125.33672553580 -219.926725535797 78 1810.99 1593.73973972590 217.250260274097 79 1670.07 1508.45167419612 161.618325803884 80 1864.44 1656.53171866965 207.908281330351 81 2052.02 1759.77633986967 292.243660130326 82 2029.6 1888.12141840814 141.478581591865 83 2070.83 2157.45338412514 -86.6233841251424 84 2293.41 2679.30972323161 -385.899723231607 85 2443.27 2512.04819969509 -68.7781996950862 86 2513.17 2325.05869746198 188.111302538024 87 2466.92 2390.87566018627 76.0443398137285 88 2502.66 2137.70189183756 364.958108162444 89 2539.91 2215.41459645775 324.49540354225 90 2482.6 2202.38993437706 280.210065622941 91 2626.15 2294.93191785381 331.218082146188 92 2656.32 2573.57260576541 82.7473942345857 93 2446.66 2018.00098667356 428.659013326444 94 2467.38 2260.03731588889 207.342684111108 95 2462.32 2908.70866695699 -446.388666956989 96 2504.58 3123.83173177044 -619.251731770443 97 2579.39 3008.8839095098 -429.493909509798 98 2649.24 3139.98261960826 -490.742619608264 99 2636.87 3110.05041830919 -473.180418309193 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.00521761298919016 0.0104352259783803 0.99478238701081 11 0.000666985766393503 0.00133397153278701 0.999333014233607 12 0.000109018963277064 0.000218037926554128 0.999890981036723 13 1.92461650155877e-05 3.84923300311753e-05 0.999980753834984 14 3.00515311559011e-05 6.01030623118022e-05 0.999969948468844 15 1.15991983850023e-05 2.31983967700045e-05 0.999988400801615 16 4.05313183110613e-06 8.10626366221227e-06 0.999995946868169 17 3.68211375031596e-06 7.36422750063193e-06 0.99999631788625 18 2.12717480554366e-06 4.25434961108731e-06 0.999997872825194 19 8.58828746667186e-07 1.71765749333437e-06 0.999999141171253 20 1.06696454570639e-06 2.13392909141278e-06 0.999998933035454 21 3.18220394967852e-07 6.36440789935703e-07 0.999999681779605 22 2.56123078860142e-07 5.12246157720283e-07 0.999999743876921 23 1.38278011229518e-07 2.76556022459035e-07 0.999999861721989 24 1.59156257646126e-07 3.18312515292253e-07 0.999999840843742 25 1.53922197767348e-07 3.07844395534697e-07 0.999999846077802 26 2.95075586104478e-07 5.90151172208955e-07 0.999999704924414 27 6.3361513727194e-06 1.26723027454388e-05 0.999993663848627 28 0.000177943919919707 0.000355887839839414 0.99982205608008 29 0.00091258111558522 0.00182516223117044 0.999087418884415 30 0.00164160961327008 0.00328321922654017 0.99835839038673 31 0.00222849919661281 0.00445699839322561 0.997771500803387 32 0.00296440811692546 0.00592881623385092 0.997035591883075 33 0.00299076566100237 0.00598153132200473 0.997009234338998 34 0.00271889692625549 0.00543779385251097 0.997281103073745 35 0.00170861376295655 0.00341722752591310 0.998291386237043 36 0.00106265037101367 0.00212530074202734 0.998937349628986 37 0.000664359199904029 0.00132871839980806 0.999335640800096 38 0.000408344756065917 0.000816689512131833 0.999591655243934 39 0.000290949287887849 0.000581898575775697 0.999709050712112 40 0.000251736086162152 0.000503472172324303 0.999748263913838 41 0.000415212190494952 0.000830424380989903 0.999584787809505 42 0.000397467289317844 0.000794934578635688 0.999602532710682 43 0.000509008796971814 0.00101801759394363 0.999490991203028 44 0.000430866252094515 0.00086173250418903 0.999569133747905 45 0.000845166075181336 0.00169033215036267 0.999154833924819 46 0.0287077736444812 0.0574155472889624 0.971292226355519 47 0.120401617434318 0.240803234868637 0.879598382565682 48 0.161381528060185 0.322763056120370 0.838618471939815 49 0.170272215586634 0.340544431173268 0.829727784413366 50 0.277380883419167 0.554761766838334 0.722619116580833 51 0.373412124018721 0.746824248037441 0.626587875981279 52 0.412754424714411 0.825508849428822 0.587245575285589 53 0.373502073271864 0.747004146543729 0.626497926728136 54 0.32662766788839 0.65325533577678 0.67337233211161 55 0.281486445427324 0.562972890854647 0.718513554572676 56 0.241525911261263 0.483051822522526 0.758474088738737 57 0.251517595846367 0.503035191692734 0.748482404153633 58 0.225792486374096 0.451584972748193 0.774207513625904 59 0.292360775606703 0.584721551213406 0.707639224393297 60 0.362816708574480 0.725633417148961 0.63718329142552 61 0.352826395119368 0.705652790238737 0.647173604880632 62 0.386221343832586 0.772442687665171 0.613778656167414 63 0.553047619845192 0.893904760309616 0.446952380154808 64 0.770894277544976 0.458211444910048 0.229105722455024 65 0.937693833898407 0.124612332203185 0.0623061661015925 66 0.99090073896844 0.0181985220631196 0.00909926103155978 67 0.99687851062924 0.0062429787415184 0.0031214893707592 68 0.998733756391652 0.00253248721669678 0.00126624360834839 69 0.999892943285628 0.000214113428743099 0.000107056714371550 70 0.999946623578438 0.000106752843124008 5.3376421562004e-05 71 0.99999456020932 1.08795813587909e-05 5.43979067939544e-06 72 0.999998642799363 2.71440127450587e-06 1.35720063725294e-06 73 0.999999849883487 3.00233026834416e-07 1.50116513417208e-07 74 0.99999998101892 3.79621608070746e-08 1.89810804035373e-08 75 0.999999947363902 1.05272196314474e-07 5.26360981572368e-08 76 0.999999799285027 4.01429945951497e-07 2.00714972975749e-07 77 0.999999753908836 4.9218232701587e-07 2.46091163507935e-07 78 0.999999746864382 5.06271236426267e-07 2.53135618213134e-07 79 0.999998887459032 2.22508193548397e-06 1.11254096774199e-06 80 0.999995784007701 8.4319845973986e-06 4.2159922986993e-06 81 0.999996616662373 6.7666752540586e-06 3.3833376270293e-06 82 0.999989729698454 2.05406030921776e-05 1.02703015460888e-05 83 0.999996493395376 7.0132092476806e-06 3.5066046238403e-06 84 0.999999850631664 2.98736672821897e-07 1.49368336410949e-07 85 0.99999852228843 2.95542314020311e-06 1.47771157010156e-06 86 0.999991105826547 1.77883469055776e-05 8.89417345278882e-06 87 0.999964288572236 7.14228555286268e-05 3.57114277643134e-05 88 0.999643979498377 0.000712041003245415 0.000356020501622707 89 0.997121338750225 0.00575732249954954 0.00287866124977477 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 58 0.725 NOK 5% type I error level 60 0.75 NOK 10% type I error level 61 0.7625 NOK

Charts produced by software:

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Parameters (Session):

par1 = 4 ; par2 = Include Quarterly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; par2 = Do not include Seasonal 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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