paper - MR seasonal dummies non-linear

*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, 21 Dec 2010 13:35:36 +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/21/t1292938592u8mjp4naeyzmpnx.htm/, Retrieved Tue, 21 Dec 2010 14:36:45 +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/21/t1292938592u8mjp4naeyzmpnx.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 «

11974 10106 12069 11412 11180 10508 11288 10928 10199 11030 11234 13747 13912 12376 12264 11675 11271 10672 10933 10379 10187 10747 10970 12175 14200 11676 11258 10872 11148 10690 10684 11658 10178 10981 10773 11665 11359 10716 12928 12317 11641 10459 10953 10703 10703 11101 11334 13268 13145 12334 13153 11289 11374 10914 11299 11284 10694 11077 11104 12820 14915 11773 11608 11468 11511 11200 11164 10960 10667 11556 11372 12333 13102 11115 12572 11557 12059 11420 11185 11113 10706 11523 11391 12634 13469 11735 13281 11968 11623 11084 11509 11134 10438 11530 11491 13093 13106 11305 13113 12203 11309 11088 11234 11619 10942 11445 11291 13281 13726 11300 11983 11092 11093 10692 10786 11166 10553 11103 10969 12090 12544 12264 13783 11214 11453 10883 10381 10348 10024 10805 10796 11907 12261 11377 12689 11474 10992 10764 12164 10409 10398 10349 10865 11630 12221 10884 12 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 10 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Aantal[t] = + 12487.4666666667 + 656.13333333334M1[t] -1018.40000000000M2[t] -64.4666666666648M3[t] -1030.8M4[t] -1190.73333333333M5[t] -1721.13333333333M6[t] -1454.73333333333M7[t] -1632.53333333334M8[t] -2088.46666666667M9[t] -1389.86666666667M10[t] -1387.73333333333M11[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) 12487.4666666667 137.436459 90.8599 0 0 M1 656.13333333334 194.364504 3.3758 0.000914 0.000457 M2 -1018.40000000000 194.364504 -5.2396 0 0 M3 -64.4666666666648 194.364504 -0.3317 0.740545 0.370272 M4 -1030.8 194.364504 -5.3034 0 0 M5 -1190.73333333333 194.364504 -6.1263 0 0 M6 -1721.13333333333 194.364504 -8.8552 0 0 M7 -1454.73333333333 194.364504 -7.4846 0 0 M8 -1632.53333333334 194.364504 -8.3993 0 0 M9 -2088.46666666667 194.364504 -10.7451 0 0 M10 -1389.86666666667 194.364504 -7.1508 0 0 M11 -1387.73333333333 194.364504 -7.1398 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.834192323463965 R-squared 0.695876832526209 Adjusted R-squared 0.675964006084472 F-TEST (value) 34.9461606850389 F-TEST (DF numerator) 11 F-TEST (DF denominator) 168 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 532.289116898187 Sum Squared Residuals 47599726.2666664 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11974 13143.5999999999 -1169.59999999989 2 10106 11469.0666666667 -1363.06666666666 3 12069 12423 -354.000000000007 4 11412 11456.6666666667 -44.6666666666724 5 11180 11296.7333333333 -116.733333333331 6 10508 10766.3333333333 -258.333333333342 7 11288 11032.7333333333 255.266666666661 8 10928 10854.9333333333 73.0666666666557 9 10199 10399 -199.999999999996 10 11030 11097.6 -67.6000000000049 11 11234 11099.7333333333 134.266666666663 12 13747 12487.4666666667 1259.53333333333 13 13912 13143.6 768.399999999992 14 12376 11469.0666666667 906.933333333333 15 12264 12423 -158.999999999999 16 11675 11456.6666666667 218.333333333334 17 11271 11296.7333333333 -25.7333333333337 18 10672 10766.3333333333 -94.3333333333327 19 10933 11032.7333333333 -99.7333333333329 20 10379 10854.9333333333 -475.933333333333 21 10187 10399 -212.000000000000 22 10747 11097.6 -350.6 23 10970 11099.7333333333 -129.733333333333 24 12175 12487.4666666667 -312.466666666667 25 14200 13143.6 1056.39999999999 26 11676 11469.0666666667 206.933333333333 27 11258 12423 -1165 28 10872 11456.6666666667 -584.666666666666 29 11148 11296.7333333333 -148.733333333334 30 10690 10766.3333333333 -76.3333333333327 31 10684 11032.7333333333 -348.733333333333 32 11658 10854.9333333333 803.066666666668 33 10178 10399 -221.000000000000 34 10981 11097.6 -116.600000000000 35 10773 11099.7333333333 -326.733333333333 36 11665 12487.4666666667 -822.466666666667 37 11359 13143.6 -1784.60000000001 38 10716 11469.0666666667 -753.066666666667 39 12928 12423 505.000000000001 40 12317 11456.6666666667 860.333333333334 41 11641 11296.7333333333 344.266666666666 42 10459 10766.3333333333 -307.333333333333 43 10953 11032.7333333333 -79.7333333333329 44 10703 10854.9333333333 -151.933333333333 45 10703 10399 304 46 11101 11097.6 3.40000000000036 47 11334 11099.7333333333 234.266666666667 48 13268 12487.4666666667 780.533333333333 49 13145 13143.6 1.39999999999199 50 12334 11469.0666666667 864.933333333333 51 13153 12423 730 52 11289 11456.6666666667 -167.666666666666 53 11374 11296.7333333333 77.2666666666664 54 10914 10766.3333333333 147.666666666667 55 11299 11032.7333333333 266.266666666667 56 11284 10854.9333333333 429.066666666668 57 10694 10399 295 58 11077 11097.6 -20.5999999999996 59 11104 11099.7333333333 4.26666666666686 60 12820 12487.4666666667 332.533333333333 61 14915 13143.6 1771.39999999999 62 11773 11469.0666666667 303.933333333333 63 11608 12423 -815 64 11468 11456.6666666667 11.3333333333337 65 11511 11296.7333333333 214.266666666666 66 11200 10766.3333333333 433.666666666667 67 11164 11032.7333333333 131.266666666667 68 10960 10854.9333333333 105.066666666667 69 10667 10399 268.000000000000 70 11556 11097.6 458.4 71 11372 11099.7333333333 272.266666666667 72 12333 12487.4666666667 -154.466666666667 73 13102 13143.6 -41.600000000008 74 11115 11469.0666666667 -354.066666666667 75 12572 12423 149.000000000001 76 11557 11456.6666666667 100.333333333334 77 12059 11296.7333333333 762.266666666667 78 11420 10766.3333333333 653.666666666668 79 11185 11032.7333333333 152.266666666667 80 11113 10854.9333333333 258.066666666667 81 10706 10399 307 82 11523 11097.6 425.400000000000 83 11391 11099.7333333333 291.266666666667 84 12634 12487.4666666667 146.533333333333 85 13469 13143.6 325.399999999992 86 11735 11469.0666666667 265.933333333333 87 13281 12423 858.000000000001 88 11968 11456.6666666667 511.333333333334 89 11623 11296.7333333333 326.266666666666 90 11084 10766.3333333333 317.666666666667 91 11509 11032.7333333333 476.266666666667 92 11134 10854.9333333333 279.066666666667 93 10438 10399 38.9999999999996 94 11530 11097.6 432.400000000000 95 11491 11099.7333333333 391.266666666667 96 13093 12487.4666666667 605.533333333333 97 13106 13143.6 -37.600000000008 98 11305 11469.0666666667 -164.066666666667 99 13113 12423 690 100 12203 11456.6666666667 746.333333333334 101 11309 11296.7333333333 12.2666666666664 102 11088 10766.3333333333 321.666666666667 103 11234 11032.7333333333 201.266666666667 104 11619 10854.9333333333 764.066666666668 105 10942 10399 543 106 11445 11097.6 347.400000000000 107 11291 11099.7333333333 191.266666666667 108 13281 12487.4666666667 793.533333333333 109 13726 13143.6 582.399999999992 110 11300 11469.0666666667 -169.066666666667 111 11983 12423 -439.999999999999 112 11092 11456.6666666667 -364.666666666666 113 11093 11296.7333333333 -203.733333333334 114 10692 10766.3333333333 -74.3333333333327 115 10786 11032.7333333333 -246.733333333333 116 11166 10854.9333333333 311.066666666667 117 10553 10399 154.000000000000 118 11103 11097.6 5.40000000000036 119 10969 11099.7333333333 -130.733333333333 120 12090 12487.4666666667 -397.466666666668 121 12544 13143.6 -599.600000000008 122 12264 11469.0666666667 794.933333333333 123 13783 12423 1360 124 11214 11456.6666666667 -242.666666666666 125 11453 11296.7333333333 156.266666666666 126 10883 10766.3333333333 116.666666666667 127 10381 11032.7333333333 -651.733333333333 128 10348 10854.9333333333 -506.933333333333 129 10024 10399 -375 130 10805 11097.6 -292.600000000000 131 10796 11099.7333333333 -303.733333333333 132 11907 12487.4666666667 -580.466666666668 133 12261 13143.6 -882.600000000008 134 11377 11469.0666666667 -92.0666666666667 135 12689 12423 266.000000000001 136 11474 11456.6666666667 17.3333333333337 137 10992 11296.7333333333 -304.733333333334 138 10764 10766.3333333333 -2.33333333333276 139 12164 11032.7333333333 1131.26666666667 140 10409 10854.9333333333 -445.933333333333 141 10398 10399 -1.00000000000038 142 10349 11097.6 -748.6 143 10865 11099.7333333333 -234.733333333333 144 11630 12487.4666666667 -857.466666666667 145 12221 13143.6 -922.600000000008 146 10884 11469.0666666667 -585.066666666667 147 12019 12423 -403.999999999999 148 11021 11456.6666666667 -435.666666666666 149 10799 11296.7333333333 -497.733333333334 150 10423 10766.3333333333 -343.333333333333 151 10484 11032.7333333333 -548.733333333333 152 10450 10854.9333333333 -404.933333333333 153 9906 10399 -493 154 11049 11097.6 -48.5999999999997 155 11281 11099.7333333333 181.266666666667 156 12485 12487.4666666667 -2.46666666666728 157 12849 13143.6 -294.600000000008 158 11380 11469.0666666667 -89.0666666666667 159 12079 12423 -343.999999999999 160 11366 11456.6666666667 -90.6666666666663 161 11328 11296.7333333333 31.2666666666664 162 10444 10766.3333333333 -322.333333333333 163 10854 11032.7333333333 -178.733333333333 164 10434 10854.9333333333 -420.933333333333 165 10137 10399 -262.000000000000 166 10992 11097.6 -105.600000000000 167 10906 11099.7333333333 -193.733333333333 168 12367 12487.4666666667 -120.466666666667 169 14371 13143.6 1227.39999999999 170 11695 11469.0666666667 225.933333333333 171 11546 12423 -877 172 10922 11456.6666666667 -534.666666666666 173 10670 11296.7333333333 -626.733333333334 174 10254 10766.3333333333 -512.333333333333 175 10573 11032.7333333333 -459.733333333333 176 10239 10854.9333333333 -615.933333333332 177 10253 10399 -146.000000000000 178 11176 11097.6 78.4000000000003 179 10719 11099.7333333333 -380.733333333333 180 11817 12487.4666666667 -670.466666666667 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.998843089422206 0.00231382115558704 0.00115691057779352 16 0.997034431693973 0.00593113661205437 0.00296556830602718 17 0.99316037394311 0.0136792521137802 0.00683962605689011 18 0.986238102064204 0.0275237958715927 0.0137618979357964 19 0.976291060465868 0.0474178790682635 0.0237089395341317 20 0.966580152296945 0.06683969540611 0.033419847703055 21 0.945731954329027 0.108536091341947 0.0542680456709733 22 0.920912655749303 0.158174688501394 0.0790873442506972 23 0.887811921889412 0.224376156221175 0.112188078110588 24 0.947311158888542 0.105377682222915 0.0526888411114575 25 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0.934549436107126 0.130901127785747 0.0654505638928735 128 0.923799398918944 0.152401202162112 0.076200601081056 129 0.907931247093133 0.184137505813733 0.0920687529068667 130 0.88684467004415 0.226310659911701 0.113155329955850 131 0.8632792927204 0.273441414559199 0.136720707279600 132 0.84777714569244 0.304445708615120 0.152222854307560 133 0.893507015555022 0.212985968889957 0.106492984444979 134 0.86565276152586 0.268694476948281 0.134347238474140 135 0.885737624759566 0.228524750480867 0.114262375240434 136 0.86676089786905 0.266478204261899 0.133239102130949 137 0.836783445478345 0.326433109043309 0.163216554521655 138 0.813736386687373 0.372527226625255 0.186263613312627 139 0.967009189720764 0.0659816205584714 0.0329908102792357 140 0.956349178957231 0.0873016420855372 0.0436508210427686 141 0.944530122248581 0.110939755502837 0.0554698777514187 142 0.959038948611404 0.0819221027771917 0.0409610513885959 143 0.943604769118128 0.112790461763745 0.0563952308818724 144 0.952301509491236 0.0953969810175272 0.0476984905087636 145 0.995101254074855 0.00979749185028977 0.00489874592514489 146 0.996515811165712 0.00696837766857659 0.00348418883428830 147 0.994689744641243 0.0106205107175140 0.00531025535875698 148 0.991540002050904 0.0169199958981912 0.00845999794909561 149 0.987401580362101 0.0251968392757971 0.0125984196378985 150 0.97998108401042 0.0400378319791615 0.0200189159895808 151 0.971693594944427 0.0566128101111466 0.0283064050555733 152 0.957369345391435 0.0852613092171299 0.0426306546085649 153 0.942776638514461 0.114446722971078 0.0572233614855388 154 0.913377238741229 0.173245522517543 0.0866227612587714 155 0.900237378047177 0.199525243905645 0.0997626219528227 156 0.876080041719638 0.247839916560724 0.123919958280362 157 0.993787250396976 0.0124254992060476 0.00621274960302379 158 0.989821113797965 0.0203577724040695 0.0101788862020348 159 0.990334549252857 0.0193309014942853 0.00966545074714264 160 0.98827831469003 0.0234433706199424 0.0117216853099712 161 0.99541540276833 0.00916919446334034 0.00458459723167017 162 0.989189638460355 0.0216207230792903 0.0108103615396451 163 0.979600654775343 0.0407986904493144 0.0203993452246572 164 0.952678719177235 0.0946425616455304 0.0473212808227652 165 0.881195609508993 0.237608780982014 0.118804390491007 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.105960264900662 NOK 5% type I error level 54 0.357615894039735 NOK 10% type I error level 75 0.496688741721854 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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