Ws7.1 inflatie -werkloosheid

*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: Thu, 19 Nov 2009 11:38:21 -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/19/t1258656095sulr0ro24v2pqmb.htm/, Retrieved Thu, 19 Nov 2009 19:41:46 +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/19/t1258656095sulr0ro24v2pqmb.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:

IsPrivate?

No (this computation is public)

User-defined keywords:

Ws7.1 inflatie -werkloosheid

Dataseries X:

» Textbox « » Textfile « » CSV «

1.4 8.2 1.2 8.0 1.0 7.5 1.7 6.8 2.4 6.5 2.0 6.6 2.1 7.6 2.0 8.0 1.8 8.1 2.7 7.7 2.3 7.5 1.9 7.6 2.0 7.8 2.3 7.8 2.8 7.8 2.4 7.5 2.3 7.5 2.7 7.1 2.7 7.5 2.9 7.5 3.0 7.6 2.2 7.7 2.3 7.7 2.8 7.9 2.8 8.1 2.8 8.2 2.2 8.2 2.6 8.2 2.8 7.9 2.5 7.3 2.4 6.9 2.3 6.6 1.9 6.7 1.7 6.9 2.0 7.0 2.1 7.1 1.7 7.2 1.8 7.1 1.8 6.9 1.8 7.0 1.3 6.8 1.3 6.4 1.3 6.7 1.2 6.6 1.4 6.4 2.2 6.3 2.9 6.2 3.1 6.5 3.5 6.8 3.6 6.8 4.4 6.4 4.1 6.1 5.1 5.8 5.8 6.1 5.9 7.2 5.4 7.3 5.5 6.9 4.8 6.1 3.2 5.8 2.7 6.2 2.1 7.1 1.9 7.7 0.6 7.9 0.7 7.7

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] = + 6.7992929704292 -0.595409398402679X[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) 6.7992929704292 1.541765 4.4101 4.2e-05 2.1e-05 X -0.595409398402679 0.214263 -2.7789 0.00721 0.003605 Multiple Linear Regression - Regression Statistics Multiple R 0.332800503518977 R-squared 0.110756175142485 Adjusted R-squared 0.0964135328060733 F-TEST (value) 7.72215973491225 F-TEST (DF numerator) 1 F-TEST (DF denominator) 62 p-value 0.00720990438056168 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.12614036542989 Sum Squared Residuals 78.627911604335 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.4 1.91693590352723 -0.51693590352723 2 1.2 2.03601778320776 -0.836017783207764 3 1 2.33372248240910 -1.33372248240910 4 1.7 2.75050906129098 -1.05050906129098 5 2.4 2.92913188081178 -0.529131880811783 6 2 2.86959094097152 -0.869590940971516 7 2.1 2.27418154256884 -0.174181542568836 8 2 2.03601778320776 -0.0360177832077644 9 1.8 1.97647684336750 -0.176476843367497 10 2.7 2.21464060272857 0.485359397271432 11 2.3 2.33372248240910 -0.0337224824091043 12 1.9 2.27418154256884 -0.374181542568837 13 2 2.1550996628883 -0.155099662888300 14 2.3 2.1550996628883 0.144900337111699 15 2.8 2.1550996628883 0.6449003371117 16 2.4 2.33372248240910 0.0662775175908958 17 2.3 2.33372248240910 -0.0337224824091043 18 2.7 2.57188624177018 0.128113758229824 19 2.7 2.33372248240910 0.366277517590896 20 2.9 2.33372248240910 0.566277517590896 21 3 2.27418154256884 0.725818457431164 22 2.2 2.21464060272857 -0.0146406027285680 23 2.3 2.21464060272857 0.0853593972714317 24 2.8 2.09555872304803 0.704441276951968 25 2.8 1.97647684336750 0.823523156632503 26 2.8 1.91693590352723 0.88306409647277 27 2.2 1.91693590352723 0.283064096472771 28 2.6 1.91693590352723 0.683064096472771 29 2.8 2.09555872304803 0.704441276951968 30 2.5 2.45280436208964 0.047195637910360 31 2.4 2.69096812145071 -0.290968121450712 32 2.3 2.86959094097152 -0.569590940971516 33 1.9 2.81005000113125 -0.910050001131247 34 1.7 2.69096812145071 -0.990968121450711 35 2 2.63142718161044 -0.631427181610444 36 2.1 2.57188624177018 -0.471886241770176 37 1.7 2.51234530192991 -0.812345301929908 38 1.8 2.57188624177018 -0.771886241770176 39 1.8 2.69096812145071 -0.890968121450711 40 1.8 2.63142718161044 -0.831427181610444 41 1.3 2.75050906129098 -1.45050906129098 42 1.3 2.98867282065205 -1.68867282065205 43 1.3 2.81005000113125 -1.51005000113125 44 1.2 2.86959094097152 -1.66959094097152 45 1.4 2.98867282065205 -1.58867282065205 46 2.2 3.04821376049232 -0.848213760492319 47 2.9 3.10775470033259 -0.207754700332587 48 3.1 2.92913188081178 0.170868119188217 49 3.5 2.75050906129098 0.74949093870902 50 3.6 2.75050906129098 0.84949093870902 51 4.4 2.98867282065205 1.41132717934795 52 4.1 3.16729564017286 0.932704359827144 53 5.1 3.34591845969366 1.75408154030634 54 5.8 3.16729564017285 2.63270435982714 55 5.9 2.51234530192991 3.38765469807009 56 5.4 2.45280436208964 2.94719563791036 57 5.5 2.69096812145071 2.80903187854929 58 4.8 3.16729564017286 1.63270435982714 59 3.2 3.34591845969366 -0.145918459693659 60 2.7 3.10775470033259 -0.407754700332587 61 2.1 2.57188624177018 -0.471886241770176 62 1.9 2.21464060272857 -0.314640602728568 63 0.6 2.09555872304803 -1.49555872304803 64 0.7 2.21464060272857 -1.51464060272857 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.05487476495759 0.10974952991518 0.94512523504241 6 0.0157044852129962 0.0314089704259924 0.984295514787004 7 0.0149770288416100 0.0299540576832201 0.98502297115839 8 0.0110406487266641 0.0220812974533281 0.988959351273336 9 0.00463431173573053 0.00926862347146106 0.99536568826427 10 0.0103383092331128 0.0206766184662256 0.989661690766887 11 0.00566546122926184 0.0113309224585237 0.994334538770738 12 0.00225509298357368 0.00451018596714736 0.997744907016426 13 0.000903231939007924 0.00180646387801585 0.999096768060992 14 0.000489914960487134 0.000979829920974268 0.999510085039513 15 0.000743763628348365 0.00148752725669673 0.999256236371652 16 0.000371620036632543 0.000743240073265086 0.999628379963367 17 0.000160199831138948 0.000320399662277897 0.999839800168861 18 0.000102128858814047 0.000204257717628095 0.999897871141186 19 7.07488441709346e-05 0.000141497688341869 0.99992925115583 20 6.85276338702973e-05 0.000137055267740595 0.99993147236613 21 7.7246112161872e-05 0.000154492224323744 0.999922753887838 22 3.04108332078833e-05 6.08216664157665e-05 0.999969589166792 23 1.19134891270325e-05 2.38269782540651e-05 0.999988086510873 24 8.93929302917981e-06 1.78785860583596e-05 0.99999106070697 25 6.5612137106992e-06 1.31224274213984e-05 0.99999343878629 26 4.53730720429082e-06 9.07461440858165e-06 0.999995462692796 27 1.74666609901726e-06 3.49333219803453e-06 0.9999982533339 28 8.91585774884334e-07 1.78317154976867e-06 0.999999108414225 29 5.93523608934406e-07 1.18704721786881e-06 0.99999940647639 30 2.29458732742378e-07 4.58917465484755e-07 0.999999770541267 31 8.02944229432409e-08 1.60588845886482e-07 0.999999919705577 32 2.84238599628386e-08 5.68477199256773e-08 0.99999997157614 33 1.33303409033169e-08 2.66606818066338e-08 0.99999998666966 34 7.9033183298663e-09 1.58066366597326e-08 0.999999992096682 35 2.8188469665673e-09 5.6376939331346e-09 0.999999997181153 36 8.95697248669437e-10 1.79139449733887e-09 0.999999999104303 37 4.58176425368841e-10 9.16352850737683e-10 0.999999999541824 38 1.92631040055146e-10 3.85262080110292e-10 0.99999999980737 39 8.34724264386317e-11 1.66944852877263e-10 0.999999999916528 40 3.4904774995334e-11 6.9809549990668e-11 0.999999999965095 41 5.5523985823355e-11 1.1104797164671e-10 0.999999999944476 42 1.05284507464494e-10 2.10569014928988e-10 0.999999999894716 43 1.85088336377084e-10 3.70176672754168e-10 0.999999999814912 44 5.74345941235107e-10 1.14869188247021e-09 0.999999999425654 45 1.72209793056495e-09 3.44419586112990e-09 0.999999998277902 46 3.05692268830710e-09 6.11384537661421e-09 0.999999996943077 47 1.27728054792601e-08 2.55456109585201e-08 0.999999987227195 48 2.92461738331463e-08 5.84923476662926e-08 0.999999970753826 49 7.99498084186314e-08 1.59899616837263e-07 0.999999920050192 50 1.81279966049386e-07 3.62559932098772e-07 0.999999818720034 51 1.85820561814267e-06 3.71641123628534e-06 0.999998141794382 52 3.85187723798283e-06 7.70375447596566e-06 0.999996148122762 53 1.91228242940512e-05 3.82456485881023e-05 0.999980877175706 54 0.000193112610894195 0.00038622522178839 0.999806887389106 55 0.00935106277678388 0.0187021255535678 0.990648937223216 56 0.121507883855253 0.243015767710505 0.878492116144747 57 0.740995289848867 0.518009420302267 0.259004710151133 58 0.94533912984641 0.109321740307179 0.0546608701535897 59 0.861129536419469 0.277740927161062 0.138870463580531 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 44 0.8 NOK 5% type I error level 50 0.909090909090909 NOK 10% type I error level 50 0.909090909090909 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/10s5pw1258655896.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/10s5pw1258655896.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/19emu1258655896.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/19emu1258655896.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/202rk1258655896.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/202rk1258655896.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/3lre31258655896.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/3lre31258655896.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/4lg2q1258655896.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/4lg2q1258655896.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/5oud61258655896.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/5oud61258655896.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/6k5yb1258655896.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/6k5yb1258655896.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/72to01258655896.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/72to01258655896.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/8pg1p1258655896.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/8pg1p1258655896.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/9tzli1258655896.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656095sulr0ro24v2pqmb/9tzli1258655896.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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