Multiple Regression met monthly dummies, een lineaire trend en een autregressie van 4 lags

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Fri, 20 Nov 2009 10:56:31 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m.htm/, Retrieved Fri, 20 Nov 2009 18:59:22 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m.htm/}, year = {2009}, } @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 = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

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No (this computation is public)

User-defined keywords:

Dataseries X:

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7.9 9.1 7.6 7.5 7.6 7.3 7.9 9 7.9 7.6 7.5 7.6 8.1 9.3 7.9 7.9 7.6 7.5 8.2 9.9 8.1 7.9 7.9 7.6 8 9.8 8.2 8.1 7.9 7.9 7.5 9.3 8 8.2 8.1 7.9 6.8 8.3 7.5 8 8.2 8.1 6.5 8 6.8 7.5 8 8.2 6.6 8.5 6.5 6.8 7.5 8 7.6 10.4 6.6 6.5 6.8 7.5 8 11.1 7.6 6.6 6.5 6.8 8.1 10.9 8 7.6 6.6 6.5 7.7 10 8.1 8 7.6 6.6 7.5 9.2 7.7 8.1 8 7.6 7.6 9.2 7.5 7.7 8.1 8 7.8 9.5 7.6 7.5 7.7 8.1 7.8 9.6 7.8 7.6 7.5 7.7 7.8 9.5 7.8 7.8 7.6 7.5 7.5 9.1 7.8 7.8 7.8 7.6 7.5 8.9 7.5 7.8 7.8 7.8 7.1 9 7.5 7.5 7.8 7.8 7.5 10.1 7.1 7.5 7.5 7.8 7.5 10.3 7.5 7.1 7.5 7.5 7.6 10.2 7.5 7.5 7.1 7.5 7.7 9.6 7.6 7.5 7.5 7.1 7.7 9.2 7.7 7.6 7.5 7.5 7.9 9.3 7.7 7.7 7.6 7.5 8.1 9.4 7.9 7.7 7.7 7.6 8.2 9.4 8.1 7.9 7.7 7.7 8.2 9.2 8.2 8.1 7.9 7.7 8.2 9 8.2 8.2 8.1 7.9 7.9 9 8.2 8.2 8.2 8.1 7.3 9 7.9 8.2 8.2 8.2 6.9 9.8 7.3 7.9 8.2 8.2 6.6 10 6.9 7.3 7.9 8.2 6.7 9.8 6.6 6.9 7.3 7.9 6.9 9.3 6.7 6.6 6.9 7.3 7 9 6.9 6.7 6.6 6.9 7.1 9 7 6.9 6.7 6.6 7.2 9.1 7.1 7 6.9 6.7 7.1 9.1 7.2 7.1 7 6.9 6.9 9.1 7.1 7.2 7.1 7 7 9.2 6.9 7.1 7. 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -0.00358137222472382 + 0.0430371388604634X[t] + 1.59792849135609Y1[t] -0.866541593526066Y2[t] -0.115378085010860Y3[t] + 0.314938073658562Y4[t] + 0.114618565818876M1[t] + 0.0598786116445835M2[t] + 0.297454773809027M3[t] + 0.163732537521505M4[t] -0.0342366300054975M5[t] + 0.029629519712166M6[t] + 0.0750588409022986M7[t] + 0.0084280845813315M8[t] -0.0150917423286754M9[t] + 0.503060824356568M10[t] -0.379811338849724M11[t] + 0.00157047152209919t + 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) -0.00358137222472382 0.668581 -0.0054 0.995747 0.497873 X 0.0430371388604634 0.055575 0.7744 0.44227 0.221135 Y1 1.59792849135609 0.148078 10.7911 0 0 Y2 -0.866541593526066 0.269219 -3.2187 0.002241 0.00112 Y3 -0.115378085010860 0.273253 -0.4222 0.674625 0.337313 Y4 0.314938073658562 0.154496 2.0385 0.046701 0.023351 M1 0.114618565818876 0.194376 0.5897 0.558013 0.279006 M2 0.0598786116445835 0.157694 0.3797 0.705734 0.352867 M3 0.297454773809027 0.162991 1.825 0.073865 0.036932 M4 0.163732537521505 0.173158 0.9456 0.348829 0.174414 M5 -0.0342366300054975 0.155588 -0.22 0.826714 0.413357 M6 0.029629519712166 0.153972 0.1924 0.848166 0.424083 M7 0.0750588409022986 0.171619 0.4374 0.663699 0.33185 M8 0.0084280845813315 0.176325 0.0478 0.962063 0.481032 M9 -0.0150917423286754 0.168227 -0.0897 0.928869 0.464434 M10 0.503060824356568 0.157899 3.186 0.002463 0.001231 M11 -0.379811338849724 0.202474 -1.8759 0.066404 0.033202 t 0.00157047152209919 0.002253 0.6971 0.488932 0.244466 Multiple Linear Regression - Regression Statistics Multiple R 0.963039353437112 R-squared 0.927444796268571 Adjusted R-squared 0.903259728358095 F-TEST (value) 38.3478268368572 F-TEST (DF numerator) 17 F-TEST (DF denominator) 51 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.205259595827174 Sum Squared Residuals 2.14870658563588 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.9 7.5716147032322 0.328385296767800 2 7.9 8.01288512534686 -0.112885125346857 3 8.1 7.96194880676678 0.138051193233223 4 8.2 8.17208540545145 0.0279145945485524 5 8 8.05234894808846 -0.0523489480884614 6 7.5 7.666951525272 -0.166951525271997 7 6.8 7.09670805838156 -0.296708058381559 8 6.5 6.38802690910635 0.111973090893651 9 6.6 6.50949811898381 0.090501881016189 10 7.6 7.45404267089778 0.145957329102217 11 8 7.92829808236166 0.0717019176383355 12 8.1 7.96768303737911 0.132316962620889 13 7.7 7.77443058382585 -0.0744305838258467 14 7.5 7.22979267384446 0.270207326155542 15 7.6 7.61040766763255 -0.0104076676325478 16 7.8 7.90191325373629 -0.101913253736286 17 7.8 7.83985019807479 -0.0398501980747887 18 7.8 7.65314936349049 0.146850636509507 19 7.5 7.69135249102222 -0.191352491022223 20 7.5 7.20129384577615 0.298706154223852 21 7.1 7.44361068233211 -0.343610682332107 22 7.5 7.40611660224678 0.09388339775322 23 7.5 7.42472895018998 0.0752710498100245 24 7.6 7.50134164326967 0.0986583567303307 25 7.7 7.5793747829622 0.120625217037794 26 7.7 7.70810436401226 -0.00810436401225563 27 7.9 7.85336274373115 0.0466372562688485 28 8.1 8.06505638998776 0.0349436100122358 29 8.2 8.04642888091472 0.153571119085279 30 8.2 8.06666698781062 0.133333012189385 31 8.2 8.05831719112769 0.141682808872312 32 7.9 8.04470671255945 -0.144706712559445 33 7.3 7.57487261713057 -0.274872617130569 34 6.9 7.43023074967045 -0.530230749670445 35 6.6 6.47290347083481 0.127096529165192 36 6.7 6.68766137234708 0.0123386276529165 37 6.9 7.05927555726046 -0.159275557260464 38 7 7.13476466790858 -0.134764667908577 39 7.1 7.25437660142686 -0.154376601426861 40 7.2 7.20808543069417 -0.00808543069417039 41 7.1 7.1362752307029 -0.0362752307028975 42 6.9 6.97522084231921 -0.0752208423192126 43 7 6.81354880886365 0.186451191136349 44 6.8 7.10740645222836 -0.307406452228362 45 6.4 6.64928047942271 -0.249280479422713 46 6.7 6.63291873044608 0.0670812695539187 47 6.6 6.61927050628903 -0.0192705062890313 48 6.4 6.54684575319587 -0.14684575319587 49 6.3 6.27812202442365 0.0218779755763539 50 6.2 6.34448724193971 -0.144487241939710 51 6.5 6.50638070936561 -0.00638070936561317 52 6.8 6.88450813124295 -0.0845081312429514 53 6.8 6.87465836406415 -0.0746583640641462 54 6.4 6.59250670494674 -0.192506704946743 55 6.1 6.04729195605271 0.0527080439472889 56 5.8 5.93104004169687 -0.131040041696870 57 6.1 5.75304070650848 0.346959293491515 58 7.2 6.97669124673891 0.223308753261090 59 7.3 7.55479899032452 -0.254798990324520 60 6.9 6.99646819380827 -0.096468193808266 61 6.1 6.33718234829564 -0.237182348295637 62 5.8 5.66996592694814 0.130034073051858 63 6.2 6.21352347107705 -0.0135234710770500 64 7.1 6.96835138888738 0.13164861111262 65 7.7 7.65043837815499 0.0495616218450148 66 7.9 7.74550457616094 0.154495423839061 67 7.7 7.59278149455217 0.107218505447832 68 7.4 7.22752603863283 0.172473961367174 69 7.5 7.06969739562232 0.430302604377684 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.816495258883685 0.367009482232629 0.183504741116315 22 0.774500963616633 0.450998072766734 0.225499036383367 23 0.699660541547621 0.600678916904757 0.300339458452379 24 0.605353671917411 0.789292656165178 0.394646328082589 25 0.525884891046345 0.94823021790731 0.474115108953655 26 0.422084534350258 0.844169068700515 0.577915465649742 27 0.337009968874212 0.674019937748423 0.662990031125788 28 0.262925289899928 0.525850579799856 0.737074710100072 29 0.273410860448021 0.546821720896041 0.72658913955198 30 0.278469477522919 0.556938955045838 0.721530522477081 31 0.461412111898618 0.922824223797236 0.538587888101382 32 0.458446452730322 0.916892905460643 0.541553547269678 33 0.381721356102745 0.76344271220549 0.618278643897255 34 0.853265002631051 0.293469994737897 0.146734997368949 35 0.802051897600116 0.395896204799769 0.197948102399884 36 0.739080763204453 0.521838473591093 0.260919236795547 37 0.794940444883794 0.410119110232412 0.205059555116206 38 0.769903570608607 0.460192858782786 0.230096429391393 39 0.71954461772717 0.56091076454566 0.28045538227283 40 0.67837386799804 0.64325226400392 0.32162613200196 41 0.604028763907724 0.791942472184551 0.395971236092276 42 0.497713283493834 0.995426566987668 0.502286716506166 43 0.615470773411963 0.769058453176073 0.384529226588037 44 0.625170175340474 0.749659649319052 0.374829824659526 45 0.742939512189661 0.514120975620678 0.257060487810339 46 0.975804873000564 0.0483902539988716 0.0241951269994358 47 0.936290702273911 0.127418595452178 0.0637092977260889 48 0.84542253764683 0.309154924706341 0.154577462353171 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 1 0.0357142857142857 OK 10% type I error level 1 0.0357142857142857 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/10jreh1258739787.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/10jreh1258739787.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/13cur1258739787.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/13cur1258739787.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/25piz1258739787.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/25piz1258739787.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/3k2dz1258739787.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/3k2dz1258739787.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/482q41258739787.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/482q41258739787.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/51a8i1258739787.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/51a8i1258739787.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/6sn4b1258739787.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/6sn4b1258739787.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/79a5b1258739787.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/79a5b1258739787.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/8w33h1258739787.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/8w33h1258739787.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/9g74x1258739787.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739950vrszalg1sg5235m/9g74x1258739787.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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