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Data X:
25.94 23688100 39.18 3940.35 0.0274 144.7 28.66 13741000 35.78 4696.69 0.0322 140.8 33.95 14143500 42.54 4572.83 0.0376 137.1 31.01 16763800 27.92 3860.66 0.0307 137.7 21.00 16634600 25.05 3400.91 0.0319 144.7 26.19 13693300 32.03 3966.11 0.0373 139.2 25.41 10545800 27.95 3766.99 0.0366 143.0 30.47 9409900 27.95 4206.35 0.0341 140.8 12.88 39182200 24.15 3672.82 0.0345 142.5 9.78 37005800 27.57 3369.63 0.0345 135.8 8.25 15818500 22.97 2597.93 0.0345 132.6 7.44 16952000 17.37 2470.52 0.0339 128.6 10.81 24563400 24.45 2772.73 0.0373 115.7 9.12 14163200 23.62 2151.83 0.0353 109.2 11.03 18184800 21.90 1840.26 0.0292 116.9 12.74 20810300 27.12 2116.24 0.0327 109.9 9.98 12843000 27.70 2110.49 0.0362 116.1 11.62 13866700 29.23 2160.54 0.0325 118.9 9.40 15119200 26.50 2027.13 0.0272 116.3 9.27 8301600 22.84 1805.43 0.0272 114.0 7.76 14039600 20.49 1498.80 0.0265 97.0 8.78 12139700 23.28 1690.20 0.0213 85.3 10.65 9649000 25.71 1930.58 0.019 84.9 10.95 8513600 26.52 1950.40 0.0155 94.6 12.36 15278600 25.51 1934.03 0.0114 97.8 10.85 15590900 23.36 1731.49 0.0114 95.0 11.84 9691100 24.15 1845.35 0.0148 110.7 12.14 10882700 20.92 1688.23 0.0164 108.5 11.65 10294800 20.38 1615.73 0.0118 110.3 8.86 16031900 21.90 1463.21 0.0107 106.3 7.63 13683600 19.21 1328.26 0.0146 97.4 7.38 8677200 19.65 1314.85 0.018 94.5 7.25 9874100 17.51 1172.06 0.0151 93.7 8.03 10725500 21.41 1329.75 0.0203 79.6 7.75 8348400 23.09 1478.78 0.022 84.9 7.16 8046200 20.70 1335.51 0.0238 80.7 7.18 10862300 19.00 1320.91 0.026 78.8 7.51 8100300 19.04 1337.52 0.0298 64.8 7.07 7287500 19.45 1341.17 0.0302 61.4 7.11 14002500 20.54 1464.31 0.0222 81.0 8.98 19037900 19.77 1595.91 0.0206 83.6 9.53 10774600 20.60 1622.80 0.0211 83.5 10.54 8960600 21.21 1735.02 0.0211 77.0 11.31 7773300 21.30 1810.45 0.0216 81.7 10.36 9579700 22.33 1786.94 0.0232 77.0 11.44 11270700 21.12 1932.21 0.0204 81.7 10.45 9492800 20.77 1960.26 0.0177 92.5 10.69 9136800 22.11 2003.37 0.0188 91.7 11.28 14487600 22.34 2066.15 0.0193 96.4 11.96 10133200 21.43 2029.82 0.0169 88.5 13.52 18659700 20.14 1994.22 0.0174 88.5 12.89 15980700 21.11 1920.15 0.0229 93.0 14.03 9732100 21.19 1986.74 0.0305 93.1 16.27 14626300 23.07 2047.79 0.0327 102.8 16.17 16904000 23.01 1887.36 0.0299 105.7 17.25 13616700 22.12 1838.10 0.0265 98.7 19.38 13772900 22.40 1896.84 0.0254 96.7 26.20 28749200 22.66 1974.99 0.0319 92.9 33.53 31408300 24.21 2096.81 0.0352 92.6 32.20 26342800 24.13 2175.44 0.0326 102.7 38.45 48909500 23.73 2062.41 0.0297 105.1 44.86 41542400 22.79 2051.72 0.0301 104.4 41.67 24857200 21.89 1999.23 0.0315 103.0 36.06 34093700 22.92 1921.65 0.0351 97.5 39.76 22555200 23.44 2068.22 0.028 103.1 36.81 19067500 22.57 2056.96 0.0253 106.2 42.65 19029100 23.27 2184.83 0.0317 103.6 46.89 15223200 24.95 2152.09 0.0364 105.5 53.61 21903700 23.45 2151.69 0.0469 87.5 57.59 33306600 23.42 2120.30 0.0435 85.2 67.82 23898100 25.30 2232.82 0.0346 98.3 71.89 23279600 23.90 2205.32 0.0342 103.8 75.51 40699800 25.73 2305.82 0.0399 106.8 68.49 37646000 24.64 2281.39 0.036 102.7 62.72 37277000 24.95 2339.79 0.0336 107.5 70.39 39246800 22.15 2322.57 0.0355 109.8 59.77 27418400 20.85 2178.88 0.0417 104.7 57.27 30318700 21.45 2172.09 0.0432 105.7 67.96 32808100 22.15 2091.47 0.0415 107.0 67.85 28668200 23.75 2183.75 0.0382 100.2 76.98 32370300 25.27 2258.43 0.0206 105.9 81.08 24171100 26.53 2366.71 0.0131 105.1 91.66 25009100 27.22 2431.77 0.0197 105.3 84.84 32084300 27.69 2415.29 0.0254 110.0 85.73 50117500 28.61 2463.93 0.0208 110.2 84.61 27522200 26.21 2416.15 0.0242 111.2 92.91 26816800 25.93 2421.64 0.0278 108.2 99.80 25136100 27.86 2525.09 0.0257 106.3 121.19 30295600 28.65 2604.52 0.0269 108.5 122.04 41526100 27.51 2603.23 0.0269 105.3 131.76 43845100 27.06 2546.27 0.0236 111.9 138.48 39188900 26.91 2596.36 0.0197 105.6 153.47 40496400 27.60 2701.50 0.0276 99.5 189.95 37438400 34.48 2859.12 0.0354 95.2 182.22 46553700 31.58 2660.96 0.0431 87.8 198.08 31771400 33.46 2652.28 0.0408 90.6 135.36 62108100 30.64 2389.86 0.0428 87.9 125.02 46645400 25.66 2271.48 0.0403 76.4 143.50 42313100 26.78 2279.10 0.0398 65.9 173.95 38841700 26.91 2412.80 0.0394 62.3 188.75 32650300 26.82 2522.66 0.0418 57.2 167.44 34281100 26.05 2292.98 0.0502 50.4 158.95 33096200 24.36 2325.55 0.056 51.9 169.53 23273800 25.94 2367.52 0.0537 58.5 113.66 43697600 25.37 2091.88 0.0494 61.4 107.59 66902300 21.23 1720.95 0.0366 38.8 92.67 44957200 19.35 1535.57 0.0107 44.9 85.35 33800900 18.61 1577.03 0.0009 38.6 90.13 33487900 16.37 1476.42 0.0003 4.0 89.31 27394900 15.56 1377.84 0.0024 25.3 105.12 25963400 17.70 1528.59 -0.0038 26.9 125.83 20952600 19.52 1717.30 -0.0074 40.8 135.81 17702900 20.26 1774.33 -0.0128 54.8 142.43 21282100 23.05 1835.04 -0.0143 49.3 163.39 18449100 22.81 1978.50 -0.021 47.4 168.21 14415700 24.04 2009.06 -0.0148 54.5 185.35 17906300 25.08 2122.42 -0.0129 53.4 188.50 22197500 27.04 2045.11 -0.0018 48.7 199.91 15856500 28.81 2144.60 0.0184 50.6 210.73 19068700 29.86 2269.15 0.0272 53.6 192.06 30855100 27.61 2147.35 0.0263 56.5 204.62 21209000 28.22 2238.26 0.0214 46.4 235.00 19541600 28.83 2397.96 0.0231 52.3 261.09 21955000 30.06 2461.19 0.0224 57.7 256.88 33725900 25.51 2257.04 0.0202 62.7 251.53 28192800 22.75 2109.24 0.0105 54.3 257.25 27377000 25.52 2254.70 0.0124 51.0 243.10 16228100 23.33 2114.03 0.0115 53.2 283.75 21278900 24.34 2368.62 0.0114 48.6 300.98 21457400 26.51 2507.41 0.0117 49.9
Names of X columns:
Apple Volume Microsoft NASDAQ Inflatie Consumentenvertrouwen
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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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') }
Compute
Summary of computational transaction
Raw Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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