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Data X:
1 149 68 12.9 1.00 1 139 39 12.2 0.89 1 148 32 12.8 0.89 1 158 62 7.4 0.89 1 128 33 6.7 0.89 1 224 52 12.6 0.78 1 159 62 14.8 0.89 1 105 77 13.3 1.00 1 159 76 11.1 0.89 1 167 41 8.2 0.78 1 165 48 11.4 1.00 1 159 63 6.4 0.78 1 119 30 10.6 0.89 1 176 78 12.0 0.89 1 54 19 6.3 0.89 0 91 31 11.3 0.89 1 163 66 11.9 0.89 1 124 35 9.3 0.67 0 137 42 9.6 1.00 1 121 45 10.0 0.78 1 153 21 6.4 0.78 1 148 25 13.8 0.89 1 221 44 10.8 0.78 1 188 69 13.8 0.89 1 149 54 11.7 0.89 1 244 74 10.9 0.33 0 148 80 16.1 1.00 0 92 42 13.4 0.89 1 150 61 9.9 0.89 1 153 41 11.5 0.67 1 94 46 8.3 0.56 1 156 39 11.7 0.89 1 132 34 9.0 0.89 1 161 51 9.7 1.00 1 105 42 10.8 0.78 1 97 31 10.3 0.78 1 151 39 10.4 0.33 0 131 20 12.7 0.78 1 166 49 9.3 0.89 1 157 53 11.8 0.89 1 111 31 5.9 0.78 1 145 39 11.4 0.89 1 162 54 13.0 0.89 1 163 49 10.8 1.00 0 59 34 12.3 0.67 1 187 46 11.3 1.00 1 109 55 11.8 0.89 0 90 42 7.9 0.89 1 105 50 12.7 0.89 0 83 13 12.3 0.78 0 116 37 11.6 0.89 0 42 25 6.7 0.78 1 148 30 10.9 0.78 0 155 28 12.1 1.00 1 125 45 13.3 0.78 1 116 35 10.1 1.00 0 128 28 5.7 0.78 1 138 41 14.3 0.67 0 49 6 8.0 0.33 0 96 45 13.3 1.00 1 164 73 9.3 1.00 1 162 17 12.5 0.78 1 99 40 7.6 0.67 1 202 64 15.9 1.00 1 186 37 9.2 0.89 0 66 25 9.1 0.89 1 183 65 11.1 0.78 1 214 100 13.0 1.00 1 188 28 14.5 0.56 0 104 35 12.2 0.67 1 177 56 12.3 0.89 1 126 29 11.4 0.89 0 76 43 8.8 0.89 0 99 59 14.6 0.89 1 139 50 12.6 0.78 1 78 3 NA 0.89 1 162 59 13.0 1.00 0 108 27 12.6 1.00 1 159 61 13.2 0.89 0 74 28 9.9 0.44 1 110 51 7.7 0.78 0 96 35 10.5 0.89 0 116 29 13.4 0.67 0 87 48 10.9 0.78 0 97 25 4.3 0.78 0 127 44 10.3 0.33 0 106 64 11.8 0.89 0 80 32 11.2 0.89 0 74 20 11.4 0.89 0 91 28 8.6 0.89 0 133 34 13.2 0.56 0 74 31 12.6 0.67 0 114 26 5.6 0.67 0 140 58 9.9 0.78 0 95 23 8.8 0.78 0 98 21 7.7 0.78 0 121 21 9.0 0.89 0 126 33 7.3 1.00 0 98 16 11.4 0.89 0 95 20 13.6 0.89 0 110 37 7.9 0.78 0 70 35 10.7 1.00 0 102 33 10.3 0.78 0 86 27 8.3 0.67 0 130 41 9.6 0.78 0 96 40 14.2 0.89 0 102 35 8.5 0.67 0 100 28 13.5 0.22 0 94 32 4.9 0.44 0 52 22 6.4 0.89 0 98 44 9.6 0.67 0 118 27 11.6 0.89 0 99 17 11.1 0.67 1 48 12 4.35 0.78 1 50 45 12.7 0.78 1 150 37 18.1 0.78 1 154 37 17.85 1.00 0 109 108 16.6 0.78 0 68 10 12.6 0.67 1 194 68 17.1 0.89 1 158 72 19.1 0.89 1 159 143 16.1 1.00 1 67 9 13.35 0.78 1 147 55 18.4 0.67 1 39 17 14.7 0.89 1 100 37 10.6 0.67 1 111 27 12.6 0.67 1 138 37 16.2 1.00 1 101 58 13.6 0.67 0 131 66 18.9 1.00 1 101 21 14.1 0.89 1 114 19 14.5 0.89 1 165 78 16.15 1.00 1 114 35 14.75 0.67 1 111 48 14.8 0.44 1 75 27 12.45 0.89 1 82 43 12.65 0.56 1 121 30 17.35 0.78 1 32 25 8.6 1.00 1 150 69 18.4 1.00 1 117 72 16.1 0.89 0 71 23 11.6 0.67 1 165 13 17.75 0.89 1 154 61 15.25 0.33 1 126 43 17.65 0.89 1 149 51 16.35 0.78 1 145 67 17.65 1.00 1 120 36 13.6 0.44 1 109 44 14.35 0.67 1 132 45 14.75 0.33 1 172 34 18.25 0.89 1 169 36 9.9 0.89 1 114 72 16 1.00 1 156 39 18.25 0.89 1 172 43 16.85 0.89 0 68 25 14.6 1.00 0 89 56 13.85 0.89 1 167 80 18.95 1.00 1 113 40 15.6 0.78 0 115 73 14.85 0.78 0 78 34 11.75 0.67 0 118 72 18.45 0.89 0 87 42 15.9 0.67 1 173 61 17.1 0.78 1 2 23 16.1 0.89 0 162 74 19.9 0.89 0 49 16 10.95 0.78 0 122 66 18.45 1.00 0 96 9 15.1 1.00 0 100 41 15 1.00 0 82 57 11.35 0.67 0 100 48 15.95 0.89 0 115 51 18.1 1.00 0 141 53 14.6 1.00 1 165 29 15.4 0.89 1 165 29 15.4 0.89 0 110 55 17.6 0.89 1 118 54 13.35 0.89 1 158 43 19.1 0.89 0 146 51 15.35 1.00 1 49 20 7.6 0.67 0 90 79 13.4 1.00 0 121 39 13.9 0.89 1 155 61 19.1 0.89 0 104 55 15.25 0.89 0 147 30 12.9 0.89 0 110 55 16.1 1.00 0 108 22 17.35 1.00 0 113 37 13.15 1.00 0 115 2 12.15 0.89 0 61 38 12.6 0.89 0 60 27 10.35 0.89 0 109 56 15.4 0.33 0 68 25 9.6 0.67 0 111 39 18.2 0.56 0 77 33 13.6 0.44 0 73 43 14.85 1.00 1 151 57 14.75 0.89 0 89 43 14.1 0.33 0 78 23 14.9 0.67 0 110 44 16.25 0.67 1 220 54 19.25 1.00 0 65 28 13.6 0.78 1 141 36 13.6 0.67 0 117 39 15.65 1.00 1 122 16 12.75 0.78 0 63 23 14.6 0.89 1 44 40 9.85 0.89 0 52 24 12.65 0.89 0 131 78 19.2 0.00 0 101 57 16.6 0.67 0 42 37 11.2 1.00 1 152 27 15.25 1.00 1 107 61 11.9 0.67 0 77 27 13.2 0.89 1 154 69 16.35 0.89 1 103 34 12.4 0.56 0 96 44 15.85 0.67 1 175 34 18.15 1.00 0 57 39 11.15 1.00 0 112 51 15.65 1.00 1 143 34 17.75 0.67 0 49 31 7.65 0.44 1 110 13 12.35 0.89 1 131 12 15.6 0.44 1 167 51 19.3 0.56 0 56 24 15.2 0.89 1 137 19 17.1 0.67 0 86 30 15.6 0.89 1 121 81 18.4 1.00 1 149 42 19.05 0.78 1 168 22 18.55 0.44 1 140 85 19.1 0.89 0 88 27 13.1 0.89 1 168 25 12.85 0.89 1 94 22 9.5 0.44 1 51 19 4.5 1.00 0 48 14 11.85 0.89 1 145 45 13.6 0.67 1 66 45 11.7 0.33 0 85 28 12.4 0.78 1 109 51 13.35 0.89 0 63 41 11.4 0.78 0 102 31 14.9 0.78 0 162 74 19.9 0.89 0 86 19 11.2 0.78 0 114 51 14.6 0.78 1 164 73 17.6 0.67 1 119 24 14.05 0.89 1 126 61 16.1 0.89 1 132 23 13.35 0.78 1 142 14 11.85 1.00 1 83 54 11.95 1.00 0 94 51 14.75 0.78 0 81 62 15.15 0.78 1 166 36 13.2 0.89 0 110 59 16.85 0.89 0 64 24 7.85 0.67 1 93 26 7.7 1.00 0 104 54 12.6 0.67 0 105 39 7.85 0.56 0 49 16 10.95 0.78 0 88 36 12.35 1.00 0 95 31 9.95 0.67 0 102 31 14.9 0.78 0 99 42 16.65 0.56 0 63 39 13.4 1.00 0 76 25 13.95 0.89 0 109 31 15.7 0.44 0 117 38 16.85 1.00 0 57 31 10.95 0.89 0 120 17 15.35 0.78 0 73 22 12.2 0.89 0 91 55 15.1 0.11 0 108 62 17.75 0.89 0 105 51 15.2 0.89 1 117 30 14.6 1.00 0 119 49 16.65 0.89 0 31 16 8.1 1.00
Names of X columns:
Curus LFM CH TOT Calculation
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
No 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
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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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6)) a<-table.element(a, signif(mysum$coefficients[i,2],6)) a<-table.element(a, signif(mysum$coefficients[i,3],4)) a<-table.element(a, signif(mysum$coefficients[i,4],6)) a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, signif(sqrt(mysum$r.squared),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, signif(mysum$r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, signif(mysum$adj.r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[1],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[2],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) 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, signif(mysum$sigma,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, signif(sum(myerror*myerror),6)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,signif(x[i],6)) a<-table.element(a,signif(x[i]-mysum$resid[i],6)) a<-table.element(a,signif(mysum$resid[i],6)) 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,signif(gqarr[mypoint-kp3+1,1],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) 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,signif(numsignificant1,6)) a<-table.element(a,signif(numsignificant1/numgqtests,6)) 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,signif(numsignificant5,6)) a<-table.element(a,signif(numsignificant5/numgqtests,6)) 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,signif(numsignificant10,6)) a<-table.element(a,signif(numsignificant10/numgqtests,6)) 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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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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