workshop 10 - multiple regression 3 (jonas poels)

*Unverified author*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 24 Dec 2010 20:02:20 +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/24/t1293220806s6a46890gvqhs1f.htm/, Retrieved Fri, 24 Dec 2010 21:00:07 +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/24/t1293220806s6a46890gvqhs1f.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 «

1 162556 162556 1081 1081 213118 213118 230380558 6282929 1 29790 29790 309 309 81767 81767 25266003 4324047 1 87550 87550 458 458 153198 153198 70164684 4108272 0 84738 0 588 0 -26007 0 -15292116 -1212617 1 54660 54660 299 299 126942 126942 37955658 1485329 1 42634 42634 156 156 157214 157214 24525384 1779876 0 40949 0 481 0 129352 0 62218312 1367203 1 42312 42312 323 323 234817 234817 75845891 2519076 1 37704 37704 452 452 60448 60448 27322496 912684 1 16275 16275 109 109 47818 47818 5212162 1443586 0 25830 0 115 0 245546 0 28237790 1220017 0 12679 0 110 0 48020 0 5282200 984885 1 18014 18014 239 239 -1710 -1710 -408690 1457425 0 43556 0 247 0 32648 0 8064056 -572920 1 24524 24524 497 497 95350 95350 47388950 929144 0 6532 0 103 0 151352 0 15589256 1151176 0 7123 0 109 0 288170 0 31410530 790090 1 20813 20813 502 502 114337 114337 57397174 774497 1 37597 37597 248 248 37884 37884 9395232 990576 0 17821 0 373 0 122844 0 45820812 454195 1 12988 12988 119 119 82340 82340 9798460 876607 1 2 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 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Dividends[t] = + 89641.0785116676 -47111.1144754121Group[t] + 0.45144769884632Costs[t] -1.88122427276785GrCosts[t] -196.042714705314Trades[t] + 3.05579768268300GrTrades[t] + 0.61601859145794GrDiv[t] + 0.00188101486872750TrDiv[t] + 0.0167804714914530`Wealth `[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) 89641.0785116676 7611.486038 11.7771 0 0 Group -47111.1144754121 18504.764751 -2.5459 0.012581 0.006291 Costs 0.45144769884632 0.493154 0.9154 0.362386 0.181193 GrCosts -1.88122427276785 0.773845 -2.431 0.017017 0.008508 Trades -196.042714705314 55.429935 -3.5368 0.00064 0.00032 GrTrades 3.05579768268300 65.59825 0.0466 0.962947 0.481474 GrDiv 0.61601859145794 0.142439 4.3248 3.9e-05 2e-05 TrDiv 0.00188101486872750 0.000408 4.6119 1.3e-05 6e-06 `Wealth ` 0.0167804714914530 0.008868 1.8922 0.061639 0.03082 Multiple Linear Regression - Regression Statistics Multiple R 0.750113577231125 R-squared 0.562670378746475 Adjusted R-squared 0.524223818636275 F-TEST (value) 14.6351293102344 F-TEST (DF numerator) 8 F-TEST (DF denominator) 91 p-value 1.51767487466259e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 36865.9122929752 Sum Squared Residuals 123677689516.594 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 213118 271556.7621898 -58438.7621897998 2 81767 110759.229434398 -28992.2294343985 3 153198 124257.388205936 28940.6117940639 4 -26007 -36490.2451990748 10483.2451990748 5 126942 81193.5983430566 45748.4016569434 6 157214 124313.427807197 32900.5721928035 7 129352 153806.745502125 -24454.7455021254 8 234817 249288.652738246 -14471.6527382465 9 60448 5337.9624847917 55110.0375152083 10 47818 61709.7612852289 -13891.7612852289 11 245546 152345.223719350 93200.7762806502 12 48020 100263.016672208 -52243.0166722077 13 -1710 -6715.76942943809 5005.76942943809 14 32648 66436.5254617733 -33788.5254617733 15 95350 75019.7922395098 20330.2077604902 16 151352 121038.433643929 30313.5663560712 17 288170 143829.841252964 144340.158747036 18 114337 107287.672095048 7049.32790495192 19 37884 -1453.94950102996 39337.9495010300 20 122844 118373.630285954 4470.36971404615 21 82340 84858.4813124563 -2518.48131245631 22 79801 68107.2041082234 11693.7958917766 23 165548 119209.602660505 46338.3973394953 24 116384 108135.953547851 8248.0464521491 25 134028 106305.814036989 27722.1859630114 26 63838 101002.479943801 -37164.4799438006 27 74996 47448.1056024594 27547.8943975406 28 31080 76381.7534582761 -45301.7534582761 29 32168 101638.627068254 -69470.6270682537 30 49857 67075.4476553549 -17218.4476553549 31 87161 102150.877740806 -14989.8777408057 32 106113 101770.696356937 4342.3036430629 33 80570 92559.4326346243 -11989.4326346243 34 102129 108970.258094779 -6841.25809477869 35 301670 112603.176138140 189066.823861860 36 102313 103806.243141918 -1493.24314191827 37 88577 102565.069179618 -13988.0691796185 38 112477 117397.576816457 -4920.57681645669 39 191778 182292.138974381 9485.86102561881 40 79804 88610.29123114 -8806.29123114004 41 128294 135921.655576866 -7627.65557686611 42 96448 101212.636821359 -4764.63682135877 43 93811 95677.0735991959 -1866.07359919588 44 117520 102311.361401423 15208.6385985768 45 69159 94014.8973874322 -24855.8973874322 46 101792 105619.004381 -3827.00438099995 47 210568 186400.776533861 24167.2234661394 48 136996 145124.613196440 -8128.6131964396 49 121920 102521.872243210 19398.1277567903 50 76403 92501.6281403057 -16098.6281403057 51 108094 115179.377526877 -7085.37752687652 52 134759 137275.533266458 -2516.53326645753 53 188873 175869.105746296 13003.8942537043 54 146216 153398.235463262 -7182.23546326161 55 156608 153921.400117165 2686.59988283512 56 61348 89997.1829398866 -28649.1829398866 57 50350 98973.520945051 -48623.5209450511 58 87720 97667.0562584108 -9947.05625841083 59 99489 95440.016737978 4048.98326202195 60 87419 86981.1288837692 437.871116230826 61 94355 100248.633800110 -5893.63380011022 62 60326 91102.1599397574 -30776.1599397574 63 94670 102858.409187576 -8188.40918757643 64 82425 85318.537357861 -2893.53735786104 65 59017 90197.688862645 -31180.6888626449 66 90829 77824.3890265762 13004.6109734238 67 80791 90729.7275906502 -9938.72759065021 68 100423 108090.887559345 -7667.88755934492 69 131116 102595.431722880 28520.5682771203 70 100269 105278.694070491 -5009.69407049134 71 27330 50952.085744255 -23622.085744255 72 39039 92295.8875835272 -53256.8875835272 73 106885 95938.5648400552 10946.4351599448 74 79285 94883.1140766618 -15598.1140766618 75 118881 97978.8228053114 20902.1771946886 76 77623 92469.857293395 -14846.8572933949 77 114768 97242.860829017 17525.1391709829 78 74015 92081.4240047587 -18066.4240047587 79 69465 88706.8589943515 -19241.8589943515 80 117869 126153.671737811 -8284.67173781054 81 60982 93952.2466475115 -32970.2466475115 82 90131 96998.0947546509 -6867.09475465085 83 138971 100921.947826691 38049.0521733088 84 39625 92813.2094663442 -53188.2094663442 85 102725 96824.5884804672 5900.41151953277 86 64239 74764.0619711108 -10525.0619711108 87 90262 96718.227321989 -6456.227321989 88 103960 96395.2667443905 7564.73325560954 89 106611 96899.3753195932 9711.62468040676 90 103345 96717.9567539067 6627.0432460933 91 95551 96283.6208096572 -732.62080965723 92 82903 94126.5774213834 -11223.5774213834 93 63593 90280.5304702878 -26687.5304702878 94 126910 120997.129116223 5912.87088377726 95 37527 90866.7383970305 -53339.7383970305 96 60247 78170.0414337687 -17923.0414337687 97 112995 97739.5382166816 15255.4617833184 98 70184 77793.8367675531 -7609.8367675531 99 130140 99191.2918170656 30948.7081829344 100 73221 90573.007135473 -17352.0071354731 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.997126615993462 0.00574676801307604 0.00287338400653802 13 0.993733447164097 0.0125331056718052 0.00626655283590261 14 0.998476992587998 0.00304601482400450 0.00152300741200225 15 0.996798403731896 0.00640319253620751 0.00320159626810375 16 0.99562132228145 0.0087573554371002 0.0043786777185501 17 0.999949412548556 0.000101174902889071 5.05874514445356e-05 18 0.999887834576238 0.000224330847523494 0.000112165423761747 19 0.999751758609837 0.000496482780326472 0.000248241390163236 20 0.999520386897057 0.000959226205886855 0.000479613102943427 21 0.999341420925218 0.00131715814956448 0.000658579074782239 22 0.998899568096578 0.00220086380684370 0.00110043190342185 23 0.998153606102164 0.00369278779567203 0.00184639389783602 24 0.997259909810265 0.005480180379469 0.0027400901897345 25 0.996237910649267 0.00752417870146629 0.00376208935073314 26 0.998617981010872 0.00276403797825659 0.00138201898912829 27 0.997697162353614 0.00460567529277194 0.00230283764638597 28 0.998938118677117 0.00212376264576631 0.00106188132288315 29 0.999948824078717 0.000102351842566959 5.11759212834793e-05 30 0.999904858010468 0.000190283979064614 9.51419895323072e-05 31 0.999921485910396 0.000157028179208382 7.85140896041911e-05 32 0.999858804773652 0.000282390452695073 0.000141195226347536 33 0.999877705022853 0.000244589954293994 0.000122294977146997 34 0.999830313166854 0.000339373666291003 0.000169686833145502 35 0.999999999994082 1.18365330178135e-11 5.91826650890677e-12 36 0.99999999998696 2.60816899040654e-11 1.30408449520327e-11 37 0.999999999976643 4.67136919906353e-11 2.33568459953177e-11 38 0.999999999947826 1.04349068301360e-10 5.21745341506798e-11 39 0.999999999868669 2.62662579211616e-10 1.31331289605808e-10 40 0.999999999733022 5.33955170451638e-10 2.66977585225819e-10 41 0.999999999990155 1.96909962565398e-11 9.84549812826988e-12 42 0.99999999999153 1.69416396153160e-11 8.47081980765799e-12 43 0.999999999978956 4.20869884073385e-11 2.10434942036693e-11 44 0.999999999976005 4.799093274538e-11 2.399546637269e-11 45 0.99999999995354 9.29213316969542e-11 4.64606658484771e-11 46 0.99999999987269 2.54621078667073e-10 1.27310539333537e-10 47 0.99999999992581 1.48380017034485e-10 7.41900085172423e-11 48 0.999999999996553 6.8939773059141e-12 3.44698865295705e-12 49 0.999999999992174 1.56513267665523e-11 7.82566338327613e-12 50 0.999999999984524 3.09528712017229e-11 1.54764356008615e-11 51 0.999999999955522 8.89550818570371e-11 4.44775409285186e-11 52 0.999999999907945 1.84110125220306e-10 9.20550626101531e-11 53 0.99999999972647 5.47058242746945e-10 2.73529121373472e-10 54 0.99999999986366 2.72679968511218e-10 1.36339984255609e-10 55 0.999999999909807 1.80386292692178e-10 9.0193146346089e-11 56 0.999999999782577 4.34845081047319e-10 2.17422540523659e-10 57 0.999999999983037 3.39250488840588e-11 1.69625244420294e-11 58 0.99999999994529 1.09418116532638e-10 5.4709058266319e-11 59 0.999999999829063 3.41873259617551e-10 1.70936629808776e-10 60 0.999999999502048 9.95904839121552e-10 4.97952419560776e-10 61 0.999999998789676 2.42064693100931e-09 1.21032346550465e-09 62 0.99999999680941 6.38117826510781e-09 3.19058913255391e-09 63 0.999999990577567 1.88448666070307e-08 9.42243330351535e-09 64 0.999999971351037 5.72979262147124e-08 2.86489631073562e-08 65 0.99999992506197 1.49876060554727e-07 7.49380302773633e-08 66 0.999999795694934 4.08610131027613e-07 2.04305065513807e-07 67 0.999999623746249 7.52507502412313e-07 3.76253751206156e-07 68 0.999999066625409 1.86674918253146e-06 9.33374591265731e-07 69 0.99999877114638 2.45770723998227e-06 1.22885361999114e-06 70 0.999996605757284 6.78848543196701e-06 3.39424271598350e-06 71 0.999993297881612 1.34042367761426e-05 6.70211838807129e-06 72 0.999986806816569 2.63863668621131e-05 1.31931834310566e-05 73 0.999975923882636 4.81522347274234e-05 2.40761173637117e-05 74 0.999938806934484 0.000122386131032283 6.11930655161417e-05 75 0.999893624130258 0.000212751739484131 0.000106375869742066 76 0.999861870868214 0.000276258263571041 0.000138129131785521 77 0.999689772454412 0.000620455091176574 0.000310227545588287 78 0.999429726091446 0.00114054781710721 0.000570273908553603 79 0.99890790485341 0.00218419029317868 0.00109209514658934 80 0.999971929949064 5.6140101871375e-05 2.80700509356875e-05 81 0.999916613238446 0.000166773523107854 8.33867615539269e-05 82 0.999875297023712 0.000249405952576544 0.000124702976288272 83 0.99965962613862 0.000680747722761924 0.000340373861380962 84 0.99963283125792 0.000734337484161349 0.000367168742080674 85 0.998536297442763 0.00292740511447479 0.00146370255723739 86 0.996519255975569 0.0069614880488625 0.00348074402443125 87 0.997974140583017 0.00405171883396538 0.00202585941698269 88 0.995384090484719 0.00923181903056236 0.00461590951528118 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 76 0.987012987012987 NOK 5% type I error level 77 1 NOK 10% type I error level 77 1 NOK

Charts produced by software:

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Parameters (Session):

par1 = 4 ; par2 = none ; par3 = 2 ; par4 = yes ;

Parameters (R input):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = yes ;

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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