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Data X:
2.1 0 1 22 23 48 23 12 41 34 2.7 0 1 22 22 50 16 45 146 61 2.1 0 1 22 21 150 33 37 182 70 2.1 0 1 20 25 154 32 37 192 69 2.1 1 0 19 30 109 37 108 263 145 2.1 1 1 20 17 68 14 10 35 23 2.1 0 1 22 27 194 52 68 439 120 2.1 0 0 21 23 158 75 72 214 147 2.1 0 1 21 23 159 72 143 341 215 2.1 0 0 21 18 67 15 9 58 24 2.4 0 0 21 18 147 29 55 292 84 1.95 0 1 21 23 39 13 17 85 30 2.1 0 1 21 19 100 40 37 200 77 2.1 0 1 21 15 111 19 27 158 46 1.95 0 1 22 20 138 24 37 199 61 2.1 0 1 24 16 101 121 58 297 178 2.4 1 1 21 24 131 93 66 227 160 2.1 0 1 22 25 101 36 21 108 57 2.25 0 1 20 25 114 23 19 86 42 2.4 0 0 21 19 165 85 78 302 163 2.25 0 1 24 19 114 41 35 148 75 2.55 0 1 25 16 111 46 48 178 94 1.95 0 1 22 19 75 18 27 120 45 2.4 0 1 21 19 82 35 43 207 78 2.1 0 1 21 23 121 17 30 157 47 2.1 0 1 22 21 32 4 25 128 29 2.4 0 0 23 22 150 28 69 296 97 2.1 0 1 24 19 117 44 72 323 116 2.1 1 1 20 20 71 10 23 79 32 2.25 0 1 22 20 165 38 13 70 50 2.25 0 1 25 3 154 57 61 146 118 2.4 0 1 22 23 126 23 43 246 66 2.1 0 0 22 14 138 26 22 145 48 2.1 0 0 21 23 149 36 51 196 86 2.4 0 0 21 20 145 22 67 199 89 2.1 0 1 21 15 120 40 36 127 76 1.95 0 0 22 13 138 18 21 91 39 2.1 0 0 22 16 109 31 44 153 75 2.25 0 0 22 7 132 11 45 299 57 2.25 0 1 21 24 172 38 34 228 72 2.4 0 0 22 17 169 24 36 190 60 2.25 0 1 23 24 114 37 72 180 109 2.25 0 1 21 24 156 37 39 212 76 2.1 0 0 21 19 172 22 43 269 65 2.1 1 1 21 25 68 15 25 130 40 2.1 1 1 19 20 89 2 56 179 58 2.7 0 1 21 28 167 43 80 243 123 2.1 0 0 21 23 113 31 40 190 71 2.1 1 0 19 27 115 29 73 299 102 2.25 1 0 18 18 78 45 34 121 80 2.7 1 0 19 28 118 25 72 137 97 2.4 1 1 21 21 87 4 42 305 46 2.1 0 0 22 19 173 31 61 157 93 2.1 0 1 22 23 2 -4 23 96 19 2.4 1 0 19 27 162 66 74 183 140 1.95 1 1 20 22 49 61 16 52 78 2.7 1 0 19 28 122 32 66 238 98 2.1 1 1 21 25 96 31 9 40 40 2.25 1 0 19 21 100 39 41 226 80 2.1 1 0 20 22 82 19 57 190 76 2.7 1 1 21 28 100 31 48 214 79 2.1 1 0 19 20 115 36 51 145 87 2.1 1 1 21 29 141 42 53 119 95 1.65 0 1 21 25 165 21 29 222 49 1.65 0 1 21 25 165 21 29 222 49 2.1 1 1 19 20 110 25 55 159 80 2.1 0 1 25 20 118 32 54 165 86 2.1 0 0 21 16 158 26 43 249 69 2.1 1 1 20 20 146 28 51 125 79 2.1 0 0 25 20 49 32 20 122 52 2.4 1 0 19 23 90 41 79 186 120 2.4 1 0 20 18 121 29 39 148 69 2.1 0 1 22 25 155 33 61 274 94 2.25 1 0 19 18 104 17 55 172 72 2.4 1 1 20 19 147 13 30 84 43 2.1 1 0 19 25 110 32 55 168 87 2.1 1 0 19 25 108 30 22 102 52 2.4 1 0 18 25 113 34 37 106 71 2.4 1 0 19 24 115 59 2 2 61 2.1 1 1 21 19 61 13 38 139 51 2.1 1 1 19 26 60 23 27 95 50 2.4 1 1 20 10 109 10 56 130 67 2.1 1 1 20 17 68 5 25 72 30 2.7 1 0 19 13 111 31 39 141 70 2.1 1 0 19 17 77 19 33 113 52 2.1 1 1 22 30 73 32 43 206 75 2.25 0 0 21 25 151 30 57 268 87 2.1 1 0 19 4 89 25 43 175 69 2.4 1 0 19 16 78 48 23 77 72 2.25 1 0 19 21 110 35 44 125 79 2.25 0 1 23 23 220 67 54 255 121 2.1 1 1 19 22 65 15 28 111 43 2.1 0 0 20 17 141 22 36 132 58 2.4 1 0 19 20 117 18 39 211 57 2.25 0 1 22 20 122 33 16 92 50 2.1 1 0 19 22 63 46 23 76 69 2.1 0 1 25 16 44 24 40 171 64 1.65 1 1 19 23 52 14 24 83 38 1.65 1 1 20 16 62 23 29 119 53 2.7 1 0 19 0 131 12 78 266 90 2.1 1 1 19 18 101 38 57 186 96 1.95 1 1 20 25 42 12 37 50 49 2.25 0 1 20 23 152 28 27 117 56 2.4 0 0 21 12 107 41 61 219 102 1.95 1 0 19 18 77 12 27 246 40 2.1 0 0 21 24 154 31 69 279 100 2.4 0 1 23 11 103 33 34 148 67 2.1 1 1 19 18 96 34 44 137 78 2.1 0 0 21 14 154 41 21 130 62 2.4 0 1 22 23 175 21 34 181 55 2.4 1 1 20 24 57 20 39 98 59 2.4 1 0 18 29 112 44 51 226 96 2.25 0 0 21 18 143 52 34 234 86 2.4 1 0 20 15 49 7 31 138 38 2.1 0 1 21 29 110 29 13 85 43 2.1 0 1 21 16 131 11 12 66 23 1.8 0 0 21 19 167 26 51 236 77 2.7 1 0 19 22 56 24 24 106 48 2.1 0 0 21 16 137 7 19 135 26 2.1 1 1 19 23 86 60 30 122 91 2.4 0 1 21 23 121 13 81 218 94 2.55 0 0 21 19 149 20 42 199 62 2.55 0 0 22 4 168 52 22 112 74 2.1 0 0 21 20 140 28 85 278 114 2.1 1 1 22 24 88 25 27 94 52 2.1 0 1 22 20 168 39 25 113 64 2.25 0 1 22 4 94 9 22 84 31 2.25 0 1 22 24 51 19 19 86 38 2.1 1 0 21 22 48 13 14 62 27 2.1 0 1 22 16 145 60 45 222 105 1.95 0 1 23 3 66 19 45 167 64 2.4 1 1 19 15 85 34 28 82 62 2.1 0 0 22 24 109 14 51 207 65 2.4 1 0 21 17 63 17 41 184 58 2.4 1 1 19 20 102 45 31 83 76 2.4 1 0 19 27 162 66 74 183 140 2.25 0 1 20 23 128 24 24 85 48 1.95 1 1 20 26 86 48 19 89 68 2.1 1 1 18 23 114 29 51 225 80 2.1 0 0 21 17 164 -2 73 237 71 2.55 0 1 21 20 119 51 24 102 76 2.1 0 0 20 22 126 2 61 221 63 2.1 0 1 20 19 132 24 23 128 46 2.1 0 1 21 24 142 40 14 91 53 1.95 0 0 21 19 83 20 54 198 74 2.25 1 1 19 23 94 19 51 204 70 2.4 1 0 19 15 81 16 62 158 78 1.95 0 1 21 27 166 20 36 138 56 2.1 1 0 19 26 110 40 59 226 100 2.1 1 1 19 22 64 27 24 44 51 1.95 0 0 24 22 93 25 26 196 52 2.1 1 0 19 18 104 49 54 83 102 2.1 1 1 19 15 105 39 39 79 78 1.95 1 1 20 22 49 61 16 52 78 2.1 1 0 19 27 88 19 36 105 55 1.95 1 1 19 10 95 67 31 116 98 2.4 1 1 19 20 102 45 31 83 76 2.4 1 0 19 17 99 30 42 196 73 2.4 1 1 19 23 63 8 39 153 47 1.95 1 0 19 19 76 19 25 157 45 2.7 1 0 20 13 109 52 31 75 83 2.1 1 1 20 27 117 22 38 106 60 1.95 1 1 19 23 57 17 31 58 48 2.1 1 0 21 16 120 33 17 75 50 1.95 1 1 19 25 73 34 22 74 56 2.1 1 0 19 2 91 22 55 185 77 2.25 1 0 19 26 108 30 62 265 91 2.7 1 1 21 20 105 25 51 131 76 2.1 0 0 22 23 117 38 30 139 68 2.4 1 0 19 22 119 26 49 196 74 1.35 1 1 19 24 31 13 16 78 29
Names of X columns:
PA programma gender age NUMERACYTOT LFM PRH CH Blogs Hours
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
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$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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