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Data X:
12.9 149 18 68 86 96 12.2 139 31 39 70 70 12.8 148 39 32 71 88 7.4 158 46 62 108 114 6.7 128 31 33 64 69 12.6 224 67 52 119 176 14.8 159 35 62 97 114 13.3 105 52 77 129 121 11.1 159 77 76 153 110 8.2 167 37 41 78 158 11.4 165 32 48 80 116 6.4 159 36 63 99 181 10.6 119 38 30 68 77 12.0 176 69 78 147 141 6.3 54 21 19 40 35 11.3 91 26 31 57 80 11.9 163 54 66 120 152 9.3 124 36 35 71 97 9.6 137 42 42 84 99 10.0 121 23 45 68 84 6.4 153 34 21 55 68 13.8 148 112 25 137 101 10.8 221 35 44 79 107 13.8 188 47 69 116 88 11.7 149 47 54 101 112 10.9 244 37 74 111 171 16.1 148 109 80 189 137 13.4 92 24 42 66 77 9.9 150 20 61 81 66 11.5 153 22 41 63 93 8.3 94 23 46 69 105 11.7 156 32 39 71 131 9.0 132 30 34 64 102 9.7 161 92 51 143 161 10.8 105 43 42 85 120 10.3 97 55 31 86 127 10.4 151 16 39 55 77 12.7 131 49 20 69 108 9.3 166 71 49 120 85 11.8 157 43 53 96 168 5.9 111 29 31 60 48 11.4 145 56 39 95 152 13.0 162 46 54 100 75 10.8 163 19 49 68 107 12.3 59 23 34 57 62 11.3 187 59 46 105 121 11.8 109 30 55 85 124 7.9 90 61 42 103 72 12.7 105 7 50 57 40 12.3 83 38 13 51 58 11.6 116 32 37 69 97 6.7 42 16 25 41 88 10.9 148 19 30 49 126 12.1 155 22 28 50 104 13.3 125 48 45 93 148 10.1 116 23 35 58 146 5.7 128 26 28 54 80 14.3 138 33 41 74 97 8.0 49 9 6 15 25 13.3 96 24 45 69 99 9.3 164 34 73 107 118 12.5 162 48 17 65 58 7.6 99 18 40 58 63 15.9 202 43 64 107 139 9.2 186 33 37 70 50 9.1 66 28 25 53 60 11.1 183 71 65 136 152 13.0 214 26 100 126 142 14.5 188 67 28 95 94 12.2 104 34 35 69 66 12.3 177 80 56 136 127 11.4 126 29 29 58 67 8.8 76 16 43 59 90 14.6 99 59 59 118 75 12.6 139 32 50 82 128 NA 78 47 3 50 41 13.0 162 43 59 102 146 12.6 108 38 27 65 69 13.2 159 29 61 90 186 9.9 74 36 28 64 81 7.7 110 32 51 83 85 10.5 96 35 35 70 54 13.4 116 21 29 50 46 10.9 87 29 48 77 106 4.3 97 12 25 37 34 10.3 127 37 44 81 60 11.8 106 37 64 101 95 11.2 80 47 32 79 57 11.4 74 51 20 71 62 8.6 91 32 28 60 36 13.2 133 21 34 55 56 12.6 74 13 31 44 54 5.6 114 14 26 40 64 9.9 140 -2 58 56 76 8.8 95 20 23 43 98 7.7 98 24 21 45 88 9.0 121 11 21 32 35 7.3 126 23 33 56 102 11.4 98 24 16 40 61 13.6 95 14 20 34 80 7.9 110 52 37 89 49 10.7 70 15 35 50 78 10.3 102 23 33 56 90 8.3 86 19 27 46 45 9.6 130 35 41 76 55 14.2 96 24 40 64 96 8.5 102 39 35 74 43 13.5 100 29 28 57 52 4.9 94 13 32 45 60 6.4 52 8 22 30 54 9.6 98 18 44 62 51 11.6 118 24 27 51 51 11.1 99 19 17 36 38 4.35 48 23 12 34 41 12.7 50 16 45 61 146 18.1 150 33 37 70 182 17.85 154 32 37 69 192 16.6 109 37 108 145 263 12.6 68 14 10 23 35 17.1 194 52 68 120 439 19.1 158 75 72 147 214 16.1 159 72 143 215 341 13.35 67 15 9 24 58 18.4 147 29 55 84 292 14.7 39 13 17 30 85 10.6 100 40 37 77 200 12.6 111 19 27 46 158 16.2 138 24 37 61 199 13.6 101 121 58 178 297 18.9 131 93 66 160 227 14.1 101 36 21 57 108 14.5 114 23 19 42 86 16.15 165 85 78 163 302 14.75 114 41 35 75 148 14.8 111 46 48 94 178 12.45 75 18 27 45 120 12.65 82 35 43 78 207 17.35 121 17 30 47 157 8.6 32 4 25 29 128 18.4 150 28 69 97 296 16.1 117 44 72 116 323 11.6 71 10 23 32 79 17.75 165 38 13 50 70 15.25 154 57 61 118 146 17.65 126 23 43 66 246 16.35 149 36 51 86 196 17.65 145 22 67 89 199 13.6 120 40 36 76 127 14.35 109 31 44 75 153 14.75 132 11 45 57 299 18.25 172 38 34 72 228 9.9 169 24 36 60 190 16 114 37 72 109 180 18.25 156 37 39 76 212 16.85 172 22 43 65 269 14.6 68 15 25 40 130 13.85 89 2 56 58 179 18.95 167 43 80 123 243 15.6 113 31 40 71 190 14.85 115 29 73 102 299 11.75 78 45 34 80 121 18.45 118 25 72 97 137 15.9 87 4 42 46 305 17.1 173 31 61 93 157 16.1 2 -4 23 19 96 19.9 162 66 74 140 183 10.95 49 61 16 78 52 18.45 122 32 66 98 238 15.1 96 31 9 40 40 15 100 39 41 80 226 11.35 82 19 57 76 190 15.95 100 31 48 79 214 18.1 115 36 51 87 145 14.6 141 42 53 95 119 15.4 165 21 29 49 222 15.4 165 21 29 49 222 17.6 110 25 55 80 159 13.35 118 32 54 86 165 19.1 158 26 43 69 249 15.35 146 28 51 79 125 7.6 49 32 20 52 122 13.4 90 41 79 120 186 13.9 121 29 39 69 148 19.1 155 33 61 94 274 15.25 104 17 55 72 172 12.9 147 13 30 43 84 16.1 110 32 55 87 168 17.35 108 30 22 52 102 13.15 113 34 37 71 106 12.15 115 59 2 61 2 12.6 61 13 38 51 139 10.35 60 23 27 50 95 15.4 109 10 56 67 130 9.6 68 5 25 30 72 18.2 111 31 39 70 141 13.6 77 19 33 52 113 14.85 73 32 43 75 206 14.75 151 30 57 87 268 14.1 89 25 43 69 175 14.9 78 48 23 72 77 16.25 110 35 44 79 125 19.25 220 67 54 121 255 13.6 65 15 28 43 111 13.6 141 22 36 58 132 15.65 117 18 39 57 211 12.75 122 33 16 50 92 14.6 63 46 23 69 76 9.85 44 24 40 64 171 12.65 52 14 24 38 83 19.2 131 12 78 90 266 16.6 101 38 57 96 186 11.2 42 12 37 49 50 15.25 152 28 27 56 117 11.9 107 41 61 102 219 13.2 77 12 27 40 246 16.35 154 31 69 100 279 12.4 103 33 34 67 148 15.85 96 34 44 78 137 18.15 175 21 34 55 181 11.15 57 20 39 59 98 15.65 112 44 51 96 226 17.75 143 52 34 86 234 7.65 49 7 31 38 138 12.35 110 29 13 43 85 15.6 131 11 12 23 66 19.3 167 26 51 77 236 15.2 56 24 24 48 106 17.1 137 7 19 26 135 15.6 86 60 30 91 122 18.4 121 13 81 94 218 19.05 149 20 42 62 199 18.55 168 52 22 74 112 19.1 140 28 85 114 278 13.1 88 25 27 52 94 12.85 168 39 25 64 113 9.5 94 9 22 31 84 4.5 51 19 19 38 86 11.85 48 13 14 27 62 13.6 145 60 45 105 222 11.7 66 19 45 64 167 12.4 85 34 28 62 82 13.35 109 14 51 65 207 11.4 63 17 41 58 184 14.9 102 45 31 76 83 19.9 162 66 74 140 183 11.2 86 48 19 68 89 14.6 114 29 51 80 225 17.6 164 -2 73 71 237 14.05 119 51 24 76 102 16.1 126 2 61 63 221 13.35 132 24 23 46 128 11.85 142 40 14 53 91 11.95 83 20 54 74 198 14.75 94 19 51 70 204 15.15 81 16 62 78 158 13.2 166 20 36 56 138 16.85 110 40 59 100 226 7.85 64 27 24 51 44 7.7 93 25 26 52 196 12.6 104 49 54 102 83 7.85 105 39 39 78 79 10.95 49 61 16 78 52 12.35 88 19 36 55 105 9.95 95 67 31 98 116 14.9 102 45 31 76 83 16.65 99 30 42 73 196 13.4 63 8 39 47 153 13.95 76 19 25 45 157 15.7 109 52 31 83 75 16.85 117 22 38 60 106 10.95 57 17 31 48 58 15.35 120 33 17 50 75 12.2 73 34 22 56 74 15.1 91 22 55 77 185 17.75 108 30 62 91 265 15.2 105 25 51 76 131 14.6 117 38 30 68 139 16.65 119 26 49 74 196 8.1 31 13 16 29 78
Names of X columns:
TOT LFM PRH CH Hours Blog
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
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12
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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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Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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