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Data X:
10.81 -0.2643 24563400 24.45 115.7 9.12 -0.2643 14163200 23.62 109.2 11.03 -0.2643 18184800 21.90 116.9 12.74 -0.1918 20810300 27.12 109.9 9.98 -0.1918 12843000 27.70 116.1 11.62 -0.1918 13866700 29.23 118.9 9.40 -0.2246 15119200 26.50 116.3 9.27 -0.2246 8301600 22.84 114.0 7.76 -0.2246 14039600 20.49 97.0 8.78 0.3654 12139700 23.28 85.3 10.65 0.3654 9649000 25.71 84.9 10.95 0.3654 8513600 26.52 94.6 12.36 0.0447 15278600 25.51 97.8 10.85 0.0447 15590900 23.36 95.0 11.84 0.0447 9691100 24.15 110.7 12.14 -0.0312 10882700 20.92 108.5 11.65 -0.0312 10294800 20.38 110.3 8.86 -0.0312 16031900 21.90 106.3 7.63 -0.0048 13683600 19.21 97.4 7.38 -0.0048 8677200 19.65 94.5 7.25 -0.0048 9874100 17.51 93.7 8.03 0.0705 10725500 21.41 79.6 7.75 0.0705 8348400 23.09 84.9 7.16 0.0705 8046200 20.70 80.7 7.18 -0.0134 10862300 19.00 78.8 7.51 -0.0134 8100300 19.04 64.8 7.07 -0.0134 7287500 19.45 61.4 7.11 0.0812 14002500 20.54 81.0 8.98 0.0812 19037900 19.77 83.6 9.53 0.0812 10774600 20.60 83.5 10.54 0.1885 8960600 21.21 77.0 11.31 0.1885 7773300 21.30 81.7 10.36 0.1885 9579700 22.33 77.0 11.44 0.3628 11270700 21.12 81.7 10.45 0.3628 9492800 20.77 92.5 10.69 0.3628 9136800 22.11 91.7 11.28 0.2942 14487600 22.34 96.4 11.96 0.2942 10133200 21.43 88.5 13.52 0.2942 18659700 20.14 88.5 12.89 0.3036 15980700 21.11 93.0 14.03 0.3036 9732100 21.19 93.1 16.27 0.3036 14626300 23.07 102.8 16.17 0.3703 16904000 23.01 105.7 17.25 0.3703 13616700 22.12 98.7 19.38 0.3703 13772900 22.40 96.7 26.20 0.7398 28749200 22.66 92.9 33.53 0.7398 31408300 24.21 92.6 32.20 0.7398 26342800 24.13 102.7 38.45 0.6988 48909500 23.73 105.1 44.86 0.6988 41542400 22.79 104.4 41.67 0.6988 24857200 21.89 103.0 36.06 0.7478 34093700 22.92 97.5 39.76 0.7478 22555200 23.44 103.1 36.81 0.7478 19067500 22.57 106.2 42.65 0.5651 19029100 23.27 103.6 46.89 0.5651 15223200 24.95 105.5 53.61 0.5651 21903700 23.45 87.5 57.59 0.6473 33306600 23.42 85.2 67.82 0.6473 23898100 25.30 98.3 71.89 0.6473 23279600 23.90 103.8 75.51 0.3441 40699800 25.73 106.8 68.49 0.3441 37646000 24.64 102.7 62.72 0.3441 37277000 24.95 107.5 70.39 0.2415 39246800 22.15 109.8 59.77 0.2415 27418400 20.85 104.7 57.27 0.2415 30318700 21.45 105.7 67.96 0.3151 32808100 22.15 107.0 67.85 0.3151 28668200 23.75 100.2 76.98 0.3151 32370300 25.27 105.9 81.08 0.239 24171100 26.53 105.1 91.66 0.239 25009100 27.22 105.3 84.84 0.239 32084300 27.69 110.0 85.73 0.2127 50117500 28.61 110.2 84.61 0.2127 27522200 26.21 111.2 92.91 0.2127 26816800 25.93 108.2 99.80 0.273 25136100 27.86 106.3 121.19 0.273 30295600 28.65 108.5 122.04 0.273 41526100 27.51 105.3 131.76 0.3657 43845100 27.06 111.9 138.48 0.3657 39188900 26.91 105.6 153.47 0.3657 40496400 27.60 99.5 189.95 0.4643 37438400 34.48 95.2 182.22 0.4643 46553700 31.58 87.8 198.08 0.4643 31771400 33.46 90.6 135.36 0.5096 62108100 30.64 87.9 125.02 0.5096 46645400 25.66 76.4 143.50 0.5096 42313100 26.78 65.9 173.95 0.3592 38841700 26.91 62.3 188.75 0.3592 32650300 26.82 57.2 167.44 0.3592 34281100 26.05 50.4 158.95 0.7439 33096200 24.36 51.9 169.53 0.7439 23273800 25.94 58.5 113.66 0.7439 43697600 25.37 61.4 107.59 0.139 66902300 21.23 38.8 92.67 0.139 44957200 19.35 44.9 85.35 0.139 33800900 18.61 38.6 90.13 0.1383 33487900 16.37 4.0 89.31 0.1383 27394900 15.56 25.3 105.12 0.1383 25963400 17.70 26.9 125.83 0.2874 20952600 19.52 40.8 135.81 0.2874 17702900 20.26 54.8 142.43 0.2874 21282100 23.05 49.3 163.39 0.0596 18449100 22.81 47.4 168.21 0.0596 14415700 24.04 54.5 185.35 0.0596 17906300 25.08 53.4 188.50 0.3201 22197500 27.04 48.7 199.91 0.3201 15856500 28.81 50.6 210.73 0.3201 19068700 29.86 53.6 192.06 0.486 30855100 27.61 56.5 204.62 0.486 21209000 28.22 46.4 235.00 0.486 19541600 28.83 52.3 261.09 0.6129 21955000 30.06 57.7 256.88 0.6129 33725900 25.51 62.7 251.53 0.6129 28192800 22.75 54.3 257.25 0.6665 27377000 25.52 51.0 243.10 0.6665 16228100 23.33 53.2 283.75 0.6665 21278900 24.34 48.6
Names of X columns:
Apple Omzetgroei Volume Microsoft Cons_vertrouwen
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
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11
12
Chart options
R Code
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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Big Analytics Cloud Computing Center
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