model 4

*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, 28 Dec 2010 21:11:46 +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/28/t1293570577x4pf60bogbr27d9.htm/, Retrieved Tue, 28 Dec 2010 22:09:48 +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/28/t1293570577x4pf60bogbr27d9.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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3,7 0 3,7 3,93 4,15 4,24 3,65 0 3,7 3,7 3,93 4,15 3,55 0 3,65 3,7 3,7 3,93 3,43 0 3,55 3,65 3,7 3,7 3,47 0 3,43 3,55 3,65 3,7 3,58 0 3,47 3,43 3,55 3,65 3,67 0 3,58 3,47 3,43 3,55 3,72 0 3,67 3,58 3,47 3,43 3,8 0 3,72 3,67 3,58 3,47 3,76 0 3,8 3,72 3,67 3,58 3,63 0 3,76 3,8 3,72 3,67 3,48 0 3,63 3,76 3,8 3,72 3,41 0 3,48 3,63 3,76 3,8 3,43 0 3,41 3,48 3,63 3,76 3,5 0 3,43 3,41 3,48 3,63 3,62 0 3,5 3,43 3,41 3,48 3,58 0 3,62 3,5 3,43 3,41 3,52 0 3,58 3,62 3,5 3,43 3,45 0 3,52 3,58 3,62 3,5 3,36 0 3,45 3,52 3,58 3,62 3,27 0 3,36 3,45 3,52 3,58 3,21 0 3,27 3,36 3,45 3,52 3,19 0 3,21 3,27 3,36 3,45 3,16 0 3,19 3,21 3,27 3,36 3,12 0 3,16 3,19 3,21 3,27 3,06 0 3,12 3,16 3,19 3,21 3,01 0 3,06 3,12 3,16 3,19 2,98 0 3,01 3,06 3,12 3,16 2,97 0 2,98 3,01 3,06 3,12 3,02 0 2,97 2,98 3,01 3,06 3,07 0 3,02 2,97 2,98 3,01 3,18 0 3,07 3,02 2,97 2,98 3,29 1 3,18 3,07 3,02 2,97 3,43 1 3,29 3,18 3,07 3,02 3,61 1 3,43 3,29 3,18 3,07 3,74 1 3,61 3,43 3,29 3,18 3,87 1 3,74 3,61 3,43 3,29 3,88 1 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 9 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 0.161444463225237 + 0.0791592781596746X[t] + 2.06064556081534Y1[t] -1.51248435858741Y2[t] + 0.399499156624229Y3[t] + 0.00955021927819122Y4[t] + 0.0378650408289318M1[t] -0.0484576135319395M2[t] + 0.0596382358271586M3[t] -0.0446390881700724M4[t] + 0.0169784404865373M5[t] + 0.0241710147035326M6[t] -0.0756529773410106M7[t] -0.0303723197500138M8[t] -0.00954782897581095M9[t] -0.0461532281989728M10[t] -0.000389226894001394M11[t] -0.000475364960323572t + 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) 0.161444463225237 0.083204 1.9404 0.057986 0.028993 X 0.0791592781596746 0.056882 1.3916 0.170194 0.085097 Y1 2.06064556081534 0.16355 12.5995 0 0 Y2 -1.51248435858741 0.33353 -4.5348 3.6e-05 1.8e-05 Y3 0.399499156624229 0.371107 1.0765 0.286867 0.143434 Y4 0.00955021927819122 0.19915 0.048 0.961943 0.480972 M1 0.0378650408289318 0.058331 0.6491 0.519217 0.259609 M2 -0.0484576135319395 0.059371 -0.8162 0.418266 0.209133 M3 0.0596382358271586 0.060524 0.9854 0.32919 0.164595 M4 -0.0446390881700724 0.059125 -0.755 0.453795 0.226897 M5 0.0169784404865373 0.06062 0.2801 0.780573 0.390287 M6 0.0241710147035326 0.058795 0.4111 0.68275 0.341375 M7 -0.0756529773410106 0.058939 -1.2836 0.205209 0.102605 M8 -0.0303723197500138 0.059955 -0.5066 0.614673 0.307337 M9 -0.00954782897581095 0.060623 -0.1575 0.875489 0.437744 M10 -0.0461532281989728 0.060888 -0.758 0.45201 0.226005 M11 -0.000389226894001394 0.060942 -0.0064 0.994929 0.497465 t -0.000475364960323572 0.001429 -0.3326 0.740843 0.370421 Multiple Linear Regression - Regression Statistics Multiple R 0.99576834325729 R-squared 0.99155459343337 Adjusted R-squared 0.988683155200714 F-TEST (value) 345.316358247658 F-TEST (DF numerator) 17 F-TEST (DF denominator) 50 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0952174421747424 Sum Squared Residuals 0.45331806471502 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3.7 3.57757361459218 0.122426385407819 2 3.65 3.74989766355371 -0.0998976635537103 3 3.55 3.66050001564694 -0.110500015646943 4 3.43 3.42311043810324 0.00688956189675844 5 3.47 3.36824861252922 0.101751387470783 6 3.58 3.59846234062266 -0.0184623406226591 7 3.67 3.61543970024126 0.0545602997587431 8 3.72 3.69416375385228 0.0258362461477184 9 3.8 3.72574848143385 0.0742515185661449 10 3.76 3.81490059240301 -0.0549005924030069 11 3.63 3.6775991351943 -0.0475991351942975 12 3.48 3.50256589205933 -0.0225658920593253 13 3.41 3.41226575169928 -0.00226575169928146 14 3.43 3.35577829777685 0.074221702223153 15 3.5 3.54931919649325 -0.0493191964932478 16 3.62 3.52916453576559 0.0908354642344062 17 3.58 3.73903172944161 -0.159031729441614 18 3.52 3.50998093858444 0.0100190614155575 19 3.45 3.39515063641853 0.0548493635814677 20 3.36 3.37162586135579 -0.0116258613557907 21 3.27 3.28803883362883 -0.0180388336288255 22 3.21 3.17308560712444 0.0369143928755603 23 3.19 3.19423566264738 -0.00423566264737936 24 3.16 3.20687123104878 -0.0468712310487771 25 3.12 3.18786175813218 -0.0678617581321828 26 3.06 3.05544945084682 0.00455054915317979 27 3.01 3.08775459685588 -0.0777545968558802 28 2.98 2.95445221852949 0.0255477814705124 29 2.97 3.00504727516210 -0.0350472751621032 30 3.02 3.01598458858034 0.00401541141965914 31 3.07 3.02137986753948 0.0486201324605214 32 3.18 3.08931172213696 0.09068827786304 33 3.29 3.35974637550926 -0.0697463755092619 34 3.43 3.40341581236597 0.0265841876340307 35 3.61 3.61524396597273 -0.00524396597272548 36 3.74 3.81932165000019 -0.0793216500001923 37 3.87 3.90932847027706 -0.0393284702770553 38 3.88 3.9670382861368 -0.0870382861367992 39 4.09 3.95229618935859 0.137703810641412 40 4.19 4.3183306434537 -0.128330643453695 41 4.2 4.27315216800057 -0.073152168000568 42 4.29 4.23321772209052 0.0567822779094791 43 4.37 4.345207083684 0.024792916315996 44 4.47 4.4236904424011 0.0463095575989001 45 4.61 4.56515580189848 0.0448441981015185 46 4.65 4.69813643263538 -0.0481364326353802 47 4.69 4.65481701441508 0.0351829855849185 48 4.82 4.73354222829309 0.0864577717069119 49 4.86 4.99563344968811 -0.135633449688110 50 4.87 4.81100026121926 0.0589997387807372 51 5.01 4.93104472601497 0.0789552739850286 52 5.03 5.11687906675682 -0.0868790667568239 53 5.13 5.01186333180455 0.118136668195449 54 5.18 5.25042079409118 -0.0704207940911813 55 5.21 5.11123229309977 0.0987677069002278 56 5.26 5.18237365467352 0.0776263453264774 57 5.25 5.28131050752958 -0.0313105075295761 58 5.2 5.1604615554712 0.0395384445287960 59 5.16 5.13810422177052 0.021895778229484 60 5.19 5.12769899859862 0.0623010014013826 61 5.39 5.26733695561119 0.122663044388810 62 5.58 5.53083604046656 0.0491639595334396 63 5.76 5.73908527563037 0.0209147243696306 64 5.89 5.79806309739116 0.091936902608842 65 5.98 5.93265688306195 0.0473431169380536 66 6.02 6.00193361603085 0.0180663839691445 67 5.62 5.90159041901696 -0.281590419016956 68 4.87 5.09883456558035 -0.228834565580345 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.74166429384776 0.516671412304479 0.258335706152240 22 0.596034891411991 0.807930217176018 0.403965108588009 23 0.444057105083052 0.888114210166104 0.555942894916948 24 0.320567637693601 0.641135275387202 0.679432362306399 25 0.310497870889949 0.620995741779899 0.68950212911005 26 0.210115102065215 0.420230204130431 0.789884897934785 27 0.164027879371093 0.328055758742185 0.835972120628907 28 0.104988031577055 0.20997606315411 0.895011968422945 29 0.0673906416709273 0.134781283341855 0.932609358329073 30 0.0416630941362966 0.0833261882725932 0.958336905863703 31 0.0222568890266107 0.0445137780532214 0.97774311097339 32 0.0176115951322445 0.035223190264489 0.982388404867755 33 0.0092342052825104 0.0184684105650208 0.99076579471749 34 0.00709021344132131 0.0141804268826426 0.992909786558679 35 0.0047942124170052 0.0095884248340104 0.995205787582995 36 0.00398268961211176 0.00796537922422352 0.996017310387888 37 0.00192965010760882 0.00385930021521765 0.998070349892391 38 0.00129384957505266 0.00258769915010532 0.998706150424947 39 0.00451371166876899 0.00902742333753798 0.99548628833123 40 0.00543436666309991 0.0108687333261998 0.9945656333369 41 0.0108542905009707 0.0217085810019413 0.98914570949903 42 0.00568019891541294 0.0113603978308259 0.994319801084587 43 0.00252471351507257 0.00504942703014514 0.997475286484927 44 0.00115393796177871 0.00230787592355742 0.998846062038221 45 0.000646263882195267 0.00129252776439053 0.999353736117805 46 0.00101450776107983 0.00202901552215966 0.99898549223892 47 0.000601725610441741 0.00120345122088348 0.999398274389558 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.370370370370370 NOK 5% type I error level 17 0.62962962962963 NOK 10% type I error level 18 0.666666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/10e1141293570695.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/10e1141293570695.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/180ls1293570695.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/180ls1293570695.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/280ls1293570695.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/280ls1293570695.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/3ia3v1293570695.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/3ia3v1293570695.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/4ia3v1293570695.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/4ia3v1293570695.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/5ia3v1293570695.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/5ia3v1293570695.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/6b1ky1293570695.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/6b1ky1293570695.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/7ma111293570695.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/7ma111293570695.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/8ma111293570695.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/8ma111293570695.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/9ma111293570695.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293570577x4pf60bogbr27d9/9ma111293570695.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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