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Data X:
48 41 23 12 34 0.75 50 146 16 45 61 1.5 150 182 33 37 70 3 154 192 32 37 69 2.25 109 263 37 108 145 3 68 35 14 10 23 1.5 194 439 52 68 120 3 158 214 75 72 147 3 159 341 72 143 215 3 67 58 15 9 24 0.75 147 292 29 55 84 3 39 85 13 17 30 2.25 100 200 40 37 77 1.5 111 158 19 27 46 1.5 138 199 24 37 61 2.25 101 297 121 58 178 3 131 227 93 66 160 3 101 108 36 21 57 1.5 114 86 23 19 42 2.25 165 302 85 78 163 2.25 114 148 41 35 75 1.5 111 178 46 48 94 2.25 75 120 18 27 45 1.5 82 207 35 43 78 2.25 121 157 17 30 47 2.25 32 128 4 25 29 3 150 296 28 69 97 3 117 323 44 72 116 3 71 79 10 23 32 1.5 165 70 38 13 50 3 154 146 57 61 118 3 126 246 23 43 66 2.25 138 145 26 22 48 1.5 149 196 36 51 86 2.25 145 199 22 67 89 2.25 120 127 40 36 76 3 138 91 18 21 39 0.75 109 153 31 44 75 2.25 132 299 11 45 57 3 172 228 38 34 72 3 169 190 24 36 60 1.5 114 180 37 72 109 2.25 156 212 37 39 76 3 172 269 22 43 65 2.25 68 130 15 25 40 1.5 89 179 2 56 58 2.25 167 243 43 80 123 2.25 113 190 31 40 71 1.5 115 299 29 73 102 2.25 78 121 45 34 80 1.5 118 137 25 72 97 2.25 87 305 4 42 46 3 173 157 31 61 93 3 2 96 4 23 19 3 162 183 66 74 140 3 49 52 61 16 78 1.5 122 238 32 66 98 2.25 96 40 31 9 40 1.5 100 226 39 41 80 2.25 82 190 19 57 76 2.25 100 214 31 48 79 2.25 115 145 36 51 87 3 141 119 42 53 95 1.5 165 222 21 29 49 2.25 165 222 21 29 49 2.25 110 159 25 55 80 3 118 165 32 54 86 2.25 158 249 26 43 69 3 146 125 28 51 79 2.25 49 122 32 20 52 1.5 90 186 41 79 120 3 121 148 29 39 69 1.5 155 274 33 61 94 3 104 172 17 55 72 3 147 84 13 30 43 3 110 168 32 55 87 3 108 102 30 22 52 2.25 113 106 34 37 71 2.25 115 2 59 2 61 0.75 61 139 13 38 51 3 60 95 23 27 50 0.75 109 130 10 56 67 1.5 68 72 5 25 30 1.5 111 141 31 39 70 3 77 113 19 33 52 1.5 73 206 32 43 75 2.25 151 268 30 57 87 3 89 175 25 43 69 3 78 77 48 23 72 1.5 110 125 35 44 79 3 220 255 67 54 121 3 65 111 15 28 43 1.5 141 132 22 36 58 1.5 117 211 18 39 57 2.25 122 92 33 16 50 1.5 63 76 46 23 69 1.5 44 171 24 40 64 2.25 52 83 14 24 38 1.5 62 119 23 29 53 2.25 131 266 12 78 90 3 101 186 38 57 96 3 42 50 12 37 49 0.75 152 117 28 27 56 1.5 107 219 41 61 102 1.5 77 246 12 27 40 2.25 154 279 31 69 100 2.25 103 148 33 34 67 1.5 96 137 34 44 78 2.25 154 130 41 21 62 0.75 175 181 21 34 55 2.25 57 98 20 39 59 0.75 112 226 44 51 96 2.25 143 234 52 34 86 3 49 138 7 31 38 0.75 110 85 29 13 43 0.75 131 66 11 12 23 3 167 236 26 51 77 3 56 106 24 24 48 3 137 135 7 19 26 3 86 122 60 30 91 1.5 121 218 13 81 94 3 149 199 20 42 62 3 168 112 52 22 74 3 140 278 28 85 114 3 88 94 25 27 52 1.5 168 113 39 25 64 2.25 94 84 9 22 31 0.75 51 86 19 19 38 0.75 48 62 13 14 27 2.25 145 222 60 45 105 3 66 167 19 45 64 2.25 85 82 34 28 62 3 109 207 14 51 65 2.25 63 184 17 41 58 3 102 83 45 31 76 1.5 162 183 66 74 140 3 128 85 24 24 48 3 86 89 48 19 68 0.75 114 225 29 51 80 1.5 164 237 2 73 71 3 119 102 51 24 76 3 126 221 2 61 63 3 132 128 24 23 46 2.25 142 91 40 14 53 2.25 83 198 20 54 74 3 94 204 19 51 70 1.5 81 158 16 62 78 2.25 166 138 20 36 56 2.25 110 226 40 59 100 2.25 64 44 27 24 51 0.75 93 196 25 26 52 2.25 104 83 49 54 102 1.5 105 79 39 39 78 2.25 49 52 61 16 78 1.5 88 105 19 36 55 0.75 95 116 67 31 98 1.5 102 83 45 31 76 1.5 99 196 30 42 73 2.25 63 153 8 39 47 1.5 76 157 19 25 45 1.5 109 75 52 31 83 3 117 106 22 38 60 2.25 57 58 17 31 48 1.5 120 75 33 17 50 0.75 73 74 34 22 56 2.25 91 185 22 55 77 3 108 265 30 62 91 3 105 131 25 51 76 1.5 117 139 38 30 68 1.5 119 196 26 49 74 2.25 31 78 13 16 29 0.75
Names of X columns:
LFM B PRH CH H Pe

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
par3 <- 'No Linear Trend' par2 <- 'Do not include Seasonal Dummies' par1 <- '6' 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 Output	view raw output of R engine
Computing time	0 seconds
R Server	Big Analytics Cloud Computing Center