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Data X:
149 11 8 7 4 152 15 18 18 9 139 19 18 20 4 148 16 12 9 5 158 24 24 19 4 128 15 16 12 4 224 17 19 16 9 159 19 16 17 8 105 19 15 9 11 159 28 28 28 4 167 26 21 20 4 165 15 18 16 6 159 26 22 22 4 119 16 19 17 8 176 24 22 12 4 54 25 25 18 4 91 22 20 20 11 163 15 16 12 4 124 21 19 16 4 137 22 18 16 6 121 27 26 21 6 153 26 24 15 4 148 26 20 17 8 221 22 19 17 5 188 21 19 17 4 149 22 23 18 9 244 20 18 15 4 148 21 16 20 7 92 20 18 13 10 150 22 21 21 4 153 21 20 12 4 94 8 15 6 7 156 22 19 13 12 146 18 27 6 4 132 20 19 19 7 161 24 7 12 5 105 17 20 14 8 97 20 20 13 5 151 23 19 12 4 131 20 19 17 9 166 22 20 19 7 157 19 18 10 4 111 15 14 10 4 145 20 17 11 4 162 22 17 11 4 163 17 8 10 4 59 14 9 7 7 187 24 22 22 4 109 17 20 12 7 90 23 20 18 4 105 25 22 20 4 83 16 22 9 4 116 18 22 16 4 42 20 16 14 8 148 18 14 11 4 155 23 24 20 4 125 24 21 17 4 116 23 20 14 4 128 13 20 8 7 138 20 18 16 12 49 20 14 11 4 96 19 19 10 4 164 22 24 15 4 162 22 19 15 5 99 15 16 10 15 202 17 16 10 5 186 19 16 18 10 66 20 14 10 9 183 22 22 22 8 214 21 21 16 4 188 21 15 10 5 104 16 14 7 4 177 20 15 16 9 126 21 14 16 4 76 20 20 16 10 99 23 21 22 4 157 15 17 13 7 139 18 14 5 4 78 22 19 18 6 162 16 16 10 7 108 17 13 8 5 159 24 26 16 4 74 13 13 8 4 110 19 18 16 4 96 20 15 14 4 116 22 18 15 4 87 19 21 9 4 97 21 17 21 6 127 15 18 7 10 106 21 20 17 7 80 24 18 18 4 74 22 25 16 4 91 20 20 16 7 133 21 19 14 4 74 19 18 15 8 114 14 12 8 11 140 25 22 22 6 95 11 16 5 14 98 17 18 13 5 121 22 23 22 4 126 20 20 18 8 98 22 20 15 9 95 15 16 11 4 110 23 22 19 4 70 20 19 19 5 102 22 23 21 4 86 16 6 4 5 130 25 19 17 4 96 18 24 10 4 102 19 19 13 7 100 25 15 15 10 94 21 18 11 4 52 22 18 20 5 98 21 22 13 4 118 22 23 18 4 99 23 18 20 4 48 20 17 15 6 50 6 6 4 4 150 15 22 9 8 154 18 20 18 5 109 24 16 12 4 68 22 16 17 17 194 21 17 12 4 158 23 20 16 4 159 20 23 17 8 67 20 18 14 4 147 18 13 13 7 39 25 22 20 4 100 16 20 16 4 111 20 20 15 5 138 14 13 10 7 101 22 16 16 4 131 26 25 21 4 101 20 16 15 7 114 17 15 16 11 165 22 19 19 7 114 22 19 9 4 111 20 24 19 4 75 17 9 7 4 82 22 22 23 4 121 17 15 14 4 32 22 22 10 4 150 21 22 16 6 117 25 24 12 8 71 11 12 10 23 165 19 21 7 4 154 24 25 20 8 126 17 26 9 6 138 22 19 14 4 149 22 21 12 4 145 17 14 10 7 120 26 28 19 4 138 19 16 16 4 109 20 21 11 4 132 19 16 15 4 172 21 16 14 10 169 24 25 11 6 114 21 21 14 5 156 19 22 15 5 172 13 9 7 4 68 24 20 22 4 89 28 19 19 5 167 27 24 22 5 113 22 22 11 5 115 23 22 19 5 78 19 12 9 4 118 18 17 11 6 87 23 18 17 4 173 21 10 12 4 2 22 22 17 4 162 17 24 10 9 49 15 18 17 18 122 21 18 13 6 96 20 23 11 5 100 26 21 19 4 82 19 21 21 11 100 28 28 24 4 115 21 17 13 10 141 19 21 16 6 165 22 21 13 8 165 21 20 15 8 110 20 18 15 6 118 19 17 11 8 158 11 7 7 4 146 17 17 13 4 49 19 14 13 9 90 20 18 12 9 121 17 14 8 5 155 21 23 7 4 104 21 20 17 4 147 12 14 9 15 110 23 17 18 10 108 22 21 17 9 113 22 23 17 7 115 21 24 18 9 61 20 21 12 6 60 18 14 14 4 109 21 24 22 7 68 24 16 19 4 111 22 21 21 7 77 20 8 10 4 73 17 17 16 15 151 19 18 11 4 89 16 17 15 9 78 19 16 12 4 110 23 22 21 4 220 8 17 22 28 65 22 21 20 4 141 23 20 15 4 117 15 20 9 4 122 17 19 15 5 63 21 8 14 4 44 25 19 11 4 52 18 11 9 12 62 23 15 18 5 131 20 13 12 4 101 21 18 11 6 42 21 19 14 6 152 24 23 10 5 107 22 20 18 4 77 22 22 11 4 154 23 19 14 4 103 17 16 16 10 96 15 11 11 7 154 24 11 8 4 175 22 21 16 4 57 19 14 13 7 112 18 21 12 4 143 21 20 17 4 49 20 21 23 12 110 19 20 14 5 131 19 19 10 8 167 16 19 16 6 56 18 18 11 17 137 23 20 16 4 86 22 21 19 5 121 23 22 17 4 149 20 19 12 5 168 24 23 17 5 140 25 16 11 6 88 25 23 19 4 168 20 18 12 4 94 23 23 8 4 51 21 20 17 6 48 23 20 13 8 145 23 23 17 10 66 11 13 7 4 85 21 21 23 5 109 27 26 18 4 63 19 18 13 4 102 21 19 17 4 162 16 18 13 16 128 22 19 13 4 86 21 18 8 7 114 22 19 16 4 164 16 13 14 4 119 18 10 13 14 126 23 21 19 5 132 24 24 15 5 142 20 21 15 5 83 20 23 8 5 94 18 18 14 7 81 4 11 7 19 166 14 16 11 16 110 22 20 17 4 64 17 20 19 4 93 23 26 17 7 104 20 21 12 9 105 18 12 12 5 49 19 15 18 14 88 20 18 16 4 95 15 14 15 16 102 24 18 20 10 99 21 16 16 5 63 19 19 12 6 76 19 7 10 4 109 27 21 28 4 117 23 24 19 4 57 23 21 18 5 120 20 20 19 4 73 17 22 8 4 91 21 17 17 5 108 23 19 16 4 105 22 20 18 4 117 16 16 12 5 119 20 20 17 8 31 16 16 13 15
Names of X columns:
LFM AMS.I1 AMS.I2 AMS.I3 AMS.A

Sample Range: (leave blank to include all observations)
From:
To:

Column Number of Endogenous Series (?)


Fixed Seasonal Effects


Type of Equation


Degree of Predetermination (lagged endogenous variables)


Degree of Seasonal Predetermination


Seasonality


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') }

Summary of computational transaction
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Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	Big Analytics Cloud Computing Center