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Data X:
149 1 11 4 0 18 139 1 19 4 1 31 148 1 16 5 0 39 158 1 24 4 1 46 128 1 15 4 1 31 224 1 17 9 1 67 159 1 19 8 0 35 105 1 19 11 1 52 159 1 28 4 1 77 167 1 26 4 1 37 165 1 15 6 1 32 159 1 26 4 1 36 119 1 16 8 1 38 176 1 24 4 0 69 54 1 25 4 0 21 91 0 22 11 0 26 163 1 15 4 1 54 124 1 21 4 0 36 137 0 22 6 1 42 121 1 27 6 0 23 153 1 26 4 1 34 148 1 26 8 1 112 221 1 22 5 0 35 188 1 21 4 1 47 149 1 22 9 1 47 244 1 20 4 1 37 148 0 21 7 1 109 92 0 20 10 0 24 150 1 22 4 1 20 153 1 21 4 0 22 94 1 8 7 0 23 156 1 22 12 0 32 132 1 20 7 1 30 161 1 24 5 1 92 105 1 17 8 1 43 97 1 20 5 1 55 151 1 23 4 0 16 131 0 20 9 1 49 166 1 22 7 1 71 157 1 19 4 0 43 111 1 15 4 1 29 145 1 20 4 1 56 162 1 22 4 1 46 163 1 17 4 1 19 59 0 14 7 1 23 187 1 24 4 0 59 109 1 17 7 1 30 90 0 23 4 1 61 105 1 25 4 0 7 83 0 16 4 1 38 116 0 18 4 1 32 42 0 20 8 1 16 148 1 18 4 1 19 155 0 23 4 1 22 125 1 24 4 1 48 116 1 23 4 1 23 128 0 13 7 0 26 138 1 20 12 1 33 49 0 20 4 0 9 96 0 19 4 1 24 164 1 22 4 1 34 162 1 22 5 0 48 99 1 15 15 0 18 202 1 17 5 1 43 186 1 19 10 0 33 66 0 20 9 1 28 183 1 22 8 0 71 214 1 21 4 1 26 188 1 21 5 1 67 104 0 16 4 0 34 177 1 20 9 0 80 126 1 21 4 0 29 76 0 20 10 0 16 99 0 23 4 1 59 139 1 18 4 0 32 162 1 16 7 0 43 108 0 17 5 1 38 159 1 24 4 0 29 74 0 13 4 0 36 110 1 19 4 1 32 96 0 20 4 0 35 116 0 22 4 0 21 87 0 19 4 0 29 97 0 21 6 1 12 127 0 15 10 0 37 106 0 21 7 1 37 80 0 24 4 1 47 74 0 22 4 0 51 91 0 20 7 0 32 133 0 21 4 0 21 74 0 19 8 1 13 114 0 14 11 1 14 140 0 25 6 1 -2 95 0 11 14 0 20 98 0 17 5 1 24 121 0 22 4 0 11 126 0 20 8 1 23 98 0 22 9 1 24 95 0 15 4 1 14 110 0 23 4 1 52 70 0 20 5 1 15 102 0 22 4 0 23 86 0 16 5 1 19 130 0 25 4 1 35 96 0 18 4 1 24 102 0 19 7 0 39 100 0 25 10 0 29 94 0 21 4 0 13 52 0 22 5 0 8 98 0 21 4 0 18 118 0 22 4 0 24 99 0 23 4 1 19 48 1 20 6 1 23 50 1 6 4 1 16 150 1 15 8 1 33 154 1 18 5 1 32 109 0 24 4 0 37 68 0 22 17 1 14 194 1 21 4 1 52 158 1 23 4 0 75 159 1 20 8 1 72 67 1 20 4 0 15 147 1 18 7 0 29 39 1 25 4 1 13 100 1 16 4 1 40 111 1 20 5 1 19 138 1 14 7 1 24 101 1 22 4 1 121 131 0 26 4 1 93 101 1 20 7 1 36 114 1 17 11 1 23 165 1 22 7 0 85 114 1 22 4 1 41 111 1 20 4 1 46 75 1 17 4 1 18 82 1 22 4 1 35 121 1 17 4 1 17 32 1 22 4 1 4 150 1 21 6 0 28 117 1 25 8 1 44 71 0 11 23 1 10 165 1 19 4 1 38 154 1 24 8 1 57 126 1 17 6 1 23 149 1 22 4 0 36 145 1 17 7 0 22 120 1 26 4 1 40 109 1 20 4 0 31 132 1 19 4 0 11 172 1 21 10 1 38 169 1 24 6 0 24 114 1 21 5 1 37 156 1 19 5 1 37 172 1 13 4 0 22 68 0 24 4 1 15 89 0 28 5 1 2 167 1 27 5 1 43 113 1 22 5 0 31 115 0 23 5 0 29 78 0 19 4 0 45 118 0 18 6 0 25 87 0 23 4 1 4 173 1 21 4 0 31 2 1 22 4 1 -4 162 0 17 9 0 66 49 0 15 18 1 61 122 0 21 6 0 32 96 0 20 5 1 31 100 0 26 4 0 39 82 0 19 11 0 19 100 0 28 4 1 31 115 0 21 10 0 36 141 0 19 6 1 42 165 1 22 8 1 21 165 1 21 8 1 21 110 0 20 6 1 25 118 1 19 8 1 32 158 1 11 4 0 26 146 0 17 4 1 28 49 1 19 9 0 32 90 0 20 9 0 41 121 0 17 5 0 29 155 1 21 4 1 33 104 0 21 4 0 17 147 0 12 15 1 13 110 0 23 10 0 32 108 0 22 9 0 30 113 0 22 7 0 34 115 0 21 9 0 59 61 0 20 6 1 13 60 0 18 4 1 23 109 0 21 7 1 10 68 0 24 4 1 5 111 0 22 7 0 31 77 0 20 4 0 19 73 0 17 15 1 32 151 1 19 4 0 30 89 0 16 9 0 25 78 0 19 4 0 48 110 0 23 4 0 35 220 1 8 28 1 67 65 0 22 4 1 15 141 1 23 4 0 22 117 0 15 4 0 18 122 1 17 5 1 33 63 0 21 4 0 46 44 1 25 4 1 24 52 0 18 12 1 14 131 0 20 4 0 12 101 0 21 6 1 38 42 0 21 6 1 12 152 1 24 5 1 28 107 1 22 4 0 41 77 0 22 4 0 12 154 1 23 4 0 31 103 1 17 10 1 33 96 0 15 7 1 34 175 1 22 4 1 21 57 0 19 7 1 20 112 0 18 4 0 44 143 1 21 4 0 52 49 0 20 12 0 7 110 1 19 5 1 29 131 1 19 8 1 11 167 1 16 6 0 26 56 0 18 17 0 24 137 1 23 4 0 7 86 0 22 5 1 60 121 1 23 4 1 13 149 1 20 5 0 20 168 1 24 5 0 52 140 1 25 6 0 28 88 0 25 4 1 25 168 1 20 4 1 39 94 1 23 4 1 9 51 1 21 6 1 19 48 0 23 8 0 13 145 1 23 10 1 60 66 1 11 4 1 19 85 0 21 5 1 34 109 1 27 4 0 14 63 0 19 4 0 17 102 0 21 4 1 45 162 0 16 16 0 66 86 0 21 7 1 48 114 0 22 4 1 29 164 1 16 4 0 -2 119 1 18 14 1 51 126 1 23 5 0 2 132 1 24 5 1 24 142 1 20 5 1 40 83 1 20 5 0 20 94 0 18 7 1 19 81 0 4 19 0 16 166 1 14 16 1 20 110 0 22 4 0 40 64 0 17 4 1 27 93 1 23 7 0 25 104 0 20 9 0 49 105 0 18 5 1 39 49 0 19 14 1 61 88 0 20 4 0 19 95 0 15 16 1 67 102 0 24 10 1 45 99 0 21 5 0 30 63 0 19 6 1 8 76 0 19 4 0 19 109 0 27 4 0 52 117 0 23 4 1 22 57 0 23 5 1 17 120 0 20 4 0 33 73 0 17 4 1 34 91 0 21 5 0 22 108 0 23 4 0 30 105 0 22 4 1 25 117 1 16 5 0 38 119 0 20 8 0 26 31 0 16 15 1 13
Names of X columns:
LFM group AMS.I1 AMS.A gender PRH
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
2
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
Pearson Chi-Squared
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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Raw Output
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