Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
10.81 24563400 -0.2643 24.45 115.7 9.12 14163200 -0.2643 23.62 109.2 11.03 18184800 -0.2643 21.90 116.9 12.74 20810300 -0.1918 27.12 109.9 9.98 12843000 -0.1918 27.70 116.1 11.62 13866700 -0.1918 29.23 118.9 9.40 15119200 -0.2246 26.50 116.3 9.27 8301600 -0.2246 22.84 114.0 7.76 14039600 -0.2246 20.49 97.0 8.78 12139700 0.3654 23.28 85.3 10.65 9649000 0.3654 25.71 84.9 10.95 8513600 0.3654 26.52 94.6 12.36 15278600 0.0447 25.51 97.8 10.85 15590900 0.0447 23.36 95.0 11.84 9691100 0.0447 24.15 110.7 12.14 10882700 -0.0312 20.92 108.5 11.65 10294800 -0.0312 20.38 110.3 8.86 16031900 -0.0312 21.90 106.3 7.63 13683600 -0.0048 19.21 97.4 7.38 8677200 -0.0048 19.65 94.5 7.25 9874100 -0.0048 17.51 93.7 8.03 10725500 0.0705 21.41 79.6 7.75 8348400 0.0705 23.09 84.9 7.16 8046200 0.0705 20.70 80.7 7.18 10862300 -0.0134 19.00 78.8 7.51 8100300 -0.0134 19.04 64.8 7.07 7287500 -0.0134 19.45 61.4 7.11 14002500 0.0812 20.54 81.0 8.98 19037900 0.0812 19.77 83.6 9.53 10774600 0.0812 20.60 83.5 10.54 8960600 0.1885 21.21 77.0 11.31 7773300 0.1885 21.30 81.7 10.36 9579700 0.1885 22.33 77.0 11.44 11270700 0.3628 21.12 81.7 10.45 9492800 0.3628 20.77 92.5 10.69 9136800 0.3628 22.11 91.7 11.28 14487600 0.2942 22.34 96.4 11.96 10133200 0.2942 21.43 88.5 13.52 18659700 0.2942 20.14 88.5 12.89 15980700 0.3036 21.11 93.0 14.03 9732100 0.3036 21.19 93.1 16.27 14626300 0.3036 23.07 102.8 16.17 16904000 0.3703 23.01 105.7 17.25 13616700 0.3703 22.12 98.7 19.38 13772900 0.3703 22.40 96.7 26.20 28749200 0.7398 22.66 92.9 33.53 31408300 0.7398 24.21 92.6 32.20 26342800 0.7398 24.13 102.7 38.45 48909500 0.6988 23.73 105.1 44.86 41542400 0.6988 22.79 104.4 41.67 24857200 0.6988 21.89 103.0 36.06 34093700 0.7478 22.92 97.5 39.76 22555200 0.7478 23.44 103.1 36.81 19067500 0.7478 22.57 106.2 42.65 19029100 0.5651 23.27 103.6 46.89 15223200 0.5651 24.95 105.5 53.61 21903700 0.5651 23.45 87.5 57.59 33306600 0.6473 23.42 85.2 67.82 23898100 0.6473 25.30 98.3 71.89 23279600 0.6473 23.90 103.8 75.51 40699800 0.3441 25.73 106.8 68.49 37646000 0.3441 24.64 102.7 62.72 37277000 0.3441 24.95 107.5 70.39 39246800 0.2415 22.15 109.8 59.77 27418400 0.2415 20.85 104.7 57.27 30318700 0.2415 21.45 105.7 67.96 32808100 0.3151 22.15 107.0 67.85 28668200 0.3151 23.75 100.2 76.98 32370300 0.3151 25.27 105.9 81.08 24171100 0.239 26.53 105.1 91.66 25009100 0.239 27.22 105.3 84.84 32084300 0.239 27.69 110.0 85.73 50117500 0.2127 28.61 110.2 84.61 27522200 0.2127 26.21 111.2 92.91 26816800 0.2127 25.93 108.2 99.80 25136100 0.273 27.86 106.3 121.19 30295600 0.273 28.65 108.5 122.04 41526100 0.273 27.51 105.3 131.76 43845100 0.3657 27.06 111.9 138.48 39188900 0.3657 26.91 105.6 153.47 40496400 0.3657 27.60 99.5 189.95 37438400 0.4643 34.48 95.2 182.22 46553700 0.4643 31.58 87.8 198.08 31771400 0.4643 33.46 90.6 135.36 62108100 0.5096 30.64 87.9 125.02 46645400 0.5096 25.66 76.4 143.50 42313100 0.5096 26.78 65.9 173.95 38841700 0.3592 26.91 62.3 188.75 32650300 0.3592 26.82 57.2 167.44 34281100 0.3592 26.05 50.4 158.95 33096200 0.7439 24.36 51.9 169.53 23273800 0.7439 25.94 58.5 113.66 43697600 0.7439 25.37 61.4 107.59 66902300 0.139 21.23 38.8 92.67 44957200 0.139 19.35 44.9 85.35 33800900 0.139 18.61 38.6 90.13 33487900 0.1383 16.37 4.0 89.31 27394900 0.1383 15.56 25.3 105.12 25963400 0.1383 17.70 26.9 125.83 20952600 0.2874 19.52 40.8 135.81 17702900 0.2874 20.26 54.8 142.43 21282100 0.2874 23.05 49.3 163.39 18449100 0.0596 22.81 47.4 168.21 14415700 0.0596 24.04 54.5 185.35 17906300 0.0596 25.08 53.4 188.50 22197500 0.3201 27.04 48.7 199.91 15856500 0.3201 28.81 50.6 210.73 19068700 0.3201 29.86 53.6 192.06 30855100 0.486 27.61 56.5 204.62 21209000 0.486 28.22 46.4 235.00 19541600 0.486 28.83 52.3 261.09 21955000 0.6129 30.06 57.7 256.88 33725900 0.6129 25.51 62.7 251.53 28192800 0.6129 22.75 54.3 257.25 27377000 0.6665 25.52 51.0 243.10 16228100 0.6665 23.33 53.2 283.75 21278900 0.6665 24.34 48.6
Names of X columns:
APPLE VOLUME REV.GROWTH MICROSOFT CONS.CONF
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
1
2
3
4
5
6
7
8
9
10
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') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation