*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Wed, 22 Dec 2010 15:22:46 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv.htm/, Retrieved Wed, 22 Dec 2010 16:20:56 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv.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 «

186448 17822 1942 16739 4872 1020 190530 22422 2547 17851 4905 1200 194207 18817 2033 17034 4971 1279 190855 22043 2049 18055 4971 1308 200779 19191 2007 18216 4930 1173 204428 23171 2660 18960 5001 1291 207617 19463 2063 17903 5059 1466 212071 22522 2113 18842 5085 1507 214239 20265 2145 18907 5111 1478 215883 24249 2866 19862 5190 1629 223484 20299 2163 18836 5076 1712 221529 25455 2157 19846 5134 1727 225247 21089 2201 19511 4804 1519 226699 26237 2838 20318 4579 1617 231406 21362 2142 19843 4526 1637 232324 26489 2253 20975 4550 1633 237192 21828 2258 20485 4566 1469 236727 27496 2979 21407 4588 1657 240698 21991 2288 20404 4564 1599 240688 27611 2431 21454 4723 1420 245283 22512 2393 21558 4553 1495 243556 28581 3244 22442 4556 1623 247826 23000 2476 21201 4542 1346 245798 28385 2490 21804 4234 1613 250479 23387 2547 22537 4341 1563 249216 30192 3461 22736 4269 2071 251896 24346 2549 21525 4217 1584 247616 30393 2496 22427 4207 1843 249994 24753 2532 23437 4267 1598 246552 31 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 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Nettoschuld[t] = + 339556.817626673 -0.0481617613139481ParafiscaleOntvangsten[t] -0.41758570531135`Niet-parafiscaleOntvangsten`[t] -0.079014694953187LopendeUitgaven[t] -23.260766663216Rentelasten[t] + 0.59991448783166KapitaalUitgaven[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) 339556.817626673 43292.692778 7.8433 0 0 ParafiscaleOntvangsten -0.0481617613139481 0.451468 -0.1067 0.91535 0.457675 `Niet-parafiscaleOntvangsten` -0.41758570531135 2.485849 -0.168 0.867079 0.43354 LopendeUitgaven -0.079014694953187 0.728579 -0.1085 0.913949 0.456974 Rentelasten -23.260766663216 6.586492 -3.5316 0.000736 0.000368 KapitaalUitgaven 0.59991448783166 1.551211 0.3867 0.700122 0.350061 Multiple Linear Regression - Regression Statistics Multiple R 0.776717115423032 R-squared 0.603289477391076 Adjusted R-squared 0.574953011490439 F-TEST (value) 21.290215918475 F-TEST (DF numerator) 5 F-TEST (DF denominator) 70 p-value 6.99107438606461e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 11873.7191966773 Sum Squared Residuals 9868964529.30806 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 186448 223850.3578924 -37402.3578924002 2 190530 222628.689405778 -32098.689405778 3 194207 221593.689258388 -27386.689258388 4 190855 221368.361561704 -30513.3615617041 5 200779 222383.239116042 -21604.2391160417 6 204428 220279.360383875 -15851.3603838745 7 207617 219546.621962367 -11929.621962367 8 212071 218724.037611439 -6653.03761143853 9 214239 218192.062555591 -3953.06255559146 10 215883 215876.634292575 6.36570742457321 11 223484 219143.025379718 4340.97462028197 12 221529 217477.278261563 4051.7217384366 13 225247 225246.919448628 0.0805513719735806 14 226699 229961.679867304 -3262.67986730438 15 231406 231769.459007616 -363.459007616426 16 232324 230826.078951515 1497.92104848526 17 237192 230616.631950384 6575.3680496163 18 236727 229570.767302102 7156.23269789848 19 240698 230727.164619166 9970.83538083406 20 240688 226507.968742248 14180.031257752 21 245283 230760.520211049 14522.4797889514 22 243556 230050.018810528 13505.9811894715 23 247826 230897.047078693 16928.9529213067 24 245798 237908.697233608 7889.30276639186 25 250479 235548.791802696 14930.2081973039 26 249216 236803.185517574 12412.8144824255 27 251896 238478.665643961 13417.3343560387 28 247616 238526.27777981 9089.72222018985 29 249994 237160.447137015 12833.5528629848 30 246552 236876.100888231 9675.89911176936 31 248771 238326.138925629 10444.8610743709 32 247551 239318.473651425 8232.52634857504 33 249745 239614.916296358 10130.0837036422 34 245742 240068.641526505 5673.35847349463 35 249019 242814.575073595 6204.42492640496 36 245841 242384.578292205 3456.42170779517 37 248771 240491.679916583 8279.32008341729 38 244723 238083.446192438 6639.55380756211 39 246878 238939.267528246 7938.73247175353 40 246014 238027.72181241 7986.27818758957 41 248496 236933.232743889 11562.7672561112 42 244351 236076.42348666 8274.57651334034 43 248016 237995.911208351 10020.0887916491 44 246509 239199.019377301 7309.9806226991 45 249426 243968.785715264 5457.21428473577 46 247840 244755.552081246 3084.44791875431 47 251035 247304.927010381 3730.07298961894 48 250161 245848.166831541 4312.83316845854 49 254278 245033.462034772 9244.53796522848 50 250801 250337.450957799 463.549042200927 51 253985 251394.782091362 2590.21790863777 52 249174 250463.335031752 -1289.33503175152 53 251287 252734.414774885 -1447.41477488466 54 247947 253429.429781653 -5482.42978165256 55 249992 254006.324100502 -4014.32410050208 56 243805 256414.71389525 -12609.71389525 57 255812 263543.240454638 -7731.24045463795 58 250417 257732.044523119 -7315.0445231195 59 253033 259853.506099276 -6820.50609927571 60 248705 258553.030091642 -9848.03009164178 61 253950 261577.913953915 -7627.91395391511 62 251484 260320.213242624 -8836.21324262381 63 251093 259665.740602664 -8572.74060266435 64 245996 260299.155158584 -14303.1551585843 65 252721 260513.092809361 -7792.0928093611 66 248019 259136.899477625 -11117.899477625 67 250464 258111.475995655 -7647.47599565543 68 245571 258558.5694206 -12987.5694205997 69 252690 258273.54070774 -5583.54070773967 70 250183 256796.9386042 -6613.93860420049 71 253639 258581.520151416 -4942.52015141576 72 254436 255365.479777894 -929.479777894365 73 265280 259870.250695415 5409.74930458459 74 268705 261136.427392119 7568.57260788119 75 270643 262697.35422867 7945.64577133006 76 271480 261894.456601271 9585.54339872862 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.152602195862955 0.305204391725909 0.847397804137045 10 0.16160512073923 0.323210241478461 0.83839487926077 11 0.0925443552573939 0.185088710514788 0.907455644742606 12 0.085990251928903 0.171980503857806 0.914009748071097 13 0.0685242482011057 0.137048496402211 0.931475751798894 14 0.0572816499632039 0.114563299926408 0.942718350036796 15 0.085963681658097 0.171927363316194 0.914036318341903 16 0.073804519864012 0.147609039728024 0.926195480135988 17 0.0944724870850097 0.188944974170019 0.90552751291499 18 0.0822557791476052 0.16451155829521 0.917744220852395 19 0.0791344339288085 0.158268867857617 0.920865566071192 20 0.474068748850204 0.948137497700408 0.525931251149796 21 0.501625160834717 0.996749678330567 0.498374839165283 22 0.465255904870885 0.93051180974177 0.534744095129115 23 0.623307729763887 0.753384540472226 0.376692270236113 24 0.55436611117106 0.89126777765788 0.44563388882894 25 0.829523181081362 0.340953637837275 0.170476818918638 26 0.831048275700119 0.337903448599762 0.168951724299881 27 0.857294400532545 0.28541119893491 0.142705599467455 28 0.858055487003078 0.283889025993844 0.141944512996922 29 0.989635663017233 0.0207286739655345 0.0103643369827673 30 0.985067550868913 0.0298648982621738 0.0149324491310869 31 0.978291988309868 0.043416023380265 0.0217080116901325 32 0.987599359107654 0.0248012817846919 0.0124006408923459 33 0.999170161456863 0.00165967708627322 0.000829838543136612 34 0.99915968903504 0.00168062192991835 0.000840310964959173 35 0.999356340051563 0.00128731989687491 0.000643659948437457 36 0.999914575366404 0.000170849267192123 8.54246335960613e-05 37 0.999978917258257 4.21654834849041e-05 2.1082741742452e-05 38 0.99998100399941 3.79920011788756e-05 1.89960005894378e-05 39 0.999970006576907 5.99868461857316e-05 2.99934230928658e-05 40 0.999974810587664 5.03788246719325e-05 2.51894123359662e-05 41 0.999971084217052 5.78315658955957e-05 2.89157829477978e-05 42 0.99996109770477 7.78045904626379e-05 3.89022952313189e-05 43 0.999929861361585 0.000140277276830627 7.01386384153137e-05 44 0.999867563808405 0.000264872383189466 0.000132436191594733 45 0.999900974106376 0.000198051787247442 9.90258936237208e-05 46 0.999867033512298 0.000265932975404515 0.000132966487702258 47 0.999819465799302 0.000361068401395079 0.000180534200697539 48 0.99975387513226 0.000492249735478768 0.000246124867739384 49 0.999729313342882 0.000541373314236076 0.000270686657118038 50 0.99980452421739 0.000390951565220379 0.00019547578261019 51 0.99989103942781 0.000217921144379738 0.000108960572189869 52 0.999759404602581 0.000481190794838058 0.000240595397419029 53 0.999759155328846 0.000481689342307984 0.000240844671153992 54 0.99977789857475 0.000444202850500633 0.000222101425250316 55 0.999967142942667 6.57141146665237e-05 3.28570573332618e-05 56 0.999974581614983 5.08367700340501e-05 2.5418385017025e-05 57 0.99998567100186 2.86579962803592e-05 1.43289981401796e-05 58 0.999980259988177 3.94800236459473e-05 1.97400118229737e-05 59 0.999986771799267 2.64564014664218e-05 1.32282007332109e-05 60 0.999981833654548 3.63326909036603e-05 1.81663454518301e-05 61 0.99993375783682 0.000132484326360958 6.6242163180479e-05 62 0.999758356298257 0.000483287403485522 0.000241643701742761 63 0.999725460663313 0.000549078673374526 0.000274539336687263 64 0.998921811527923 0.00215637694415461 0.00107818847207731 65 0.996485706823846 0.00702858635230723 0.00351429317615362 66 0.987323330971868 0.0253533380562641 0.012676669028132 67 0.988847179201368 0.0223056415972636 0.0111528207986318 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 33 0.559322033898305 NOK 5% type I error level 39 0.661016949152542 NOK 10% type I error level 39 0.661016949152542 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/10miuz1293031357.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/10miuz1293031357.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/1xyen1293031357.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/1xyen1293031357.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/2q8e81293031357.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/2q8e81293031357.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/3q8e81293031357.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/3q8e81293031357.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/4q8e81293031357.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/4q8e81293031357.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/5jzdb1293031357.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/5jzdb1293031357.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/6jzdb1293031357.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/6jzdb1293031357.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/7t8ue1293031357.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/7t8ue1293031357.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/8t8ue1293031357.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/8t8ue1293031357.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/9miuz1293031357.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031253e8tul81xfj5o4gv/9miuz1293031357.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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