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13 12 14 12 53 41 38 1 16 11 18 11 86 39 32 2 19 15 11 14 66 30 35 3 15 6 12 12 67 31 33 4 14 13 16 21 76 34 37 5 13 10 18 12 78 35 29 6 19 12 14 22 53 39 31 7 15 14 14 11 80 34 36 8 14 12 15 10 74 36 35 9 15 6 15 13 76 37 38 10 16 10 17 10 79 38 31 11 16 12 19 8 54 36 34 12 16 12 10 15 67 38 35 13 16 11 16 14 54 39 38 14 17 15 18 10 87 33 37 15 15 12 14 14 58 32 33 16 15 10 14 14 75 36 32 17 20 12 17 11 88 38 38 18 18 11 14 10 64 39 38 19 16 12 16 13 57 32 32 20 16 11 18 7 66 32 33 21 16 12 11 14 68 31 31 22 19 13 14 12 54 39 38 23 16 11 12 14 56 37 39 24 17 9 17 11 86 39 32 25 17 13 9 9 80 41 32 26 16 10 16 11 76 36 35 27 15 14 14 15 69 33 37 28 16 12 15 14 78 33 33 29 14 10 11 13 67 34 33 30 15 12 16 9 80 31 28 31 12 8 13 15 54 27 32 32 14 10 17 10 71 37 31 33 16 12 15 11 84 34 37 34 14 12 14 13 74 34 30 35 7 7 16 8 71 32 33 36 10 6 9 20 63 29 31 37 14 12 15 12 71 36 33 38 16 10 17 10 76 29 31 39 16 10 13 10 69 35 33 40 16 10 15 9 74 37 32 41 14 12 16 14 75 34 33 42 20 15 16 8 54 38 32 43 14 10 12 14 52 35 33 44 14 10 12 11 69 38 28 45 11 12 11 13 68 37 35 46 14 13 15 9 65 38 39 47 15 11 15 11 75 33 34 48 16 11 17 15 74 36 38 49 14 12 13 11 75 38 32 50 16 14 16 10 72 32 38 51 14 10 14 14 67 32 30 52 12 12 11 18 63 32 33 53 16 13 12 14 62 34 38 54 9 5 12 11 63 32 32 55 14 6 15 12 76 37 32 56 16 12 16 13 74 39 34 57 16 12 15 9 67 29 34 58 15 11 12 10 73 37 36 59 16 10 12 15 70 35 34 60 12 7 8 20 53 30 28 61 16 12 13 12 77 38 34 62 16 14 11 12 77 34 35 63 14 11 14 14 52 31 35 64 16 12 15 13 54 34 31 65 17 13 10 11 80 35 37 66 18 14 11 17 66 36 35 67 18 11 12 12 73 30 27 68 12 12 15 13 63 39 40 69 16 12 15 14 69 35 37 70 10 8 14 13 67 38 36 71 14 11 16 15 54 31 38 72 18 14 15 13 81 34 39 73 18 14 15 10 69 38 41 74 16 12 13 11 84 34 27 75 17 9 12 19 80 39 30 76 16 13 17 13 70 37 37 77 16 11 13 17 69 34 31 78 13 12 15 13 77 28 31 79 16 12 13 9 54 37 27 80 16 12 15 11 79 33 36 81 20 12 16 10 30 37 38 82 16 12 15 9 71 35 37 83 15 12 16 12 73 37 33 84 15 11 15 12 72 32 34 85 16 10 14 13 77 33 31 86 14 9 15 13 75 38 39 87 16 12 14 12 69 33 34 88 16 12 13 15 54 29 32 89 15 12 7 22 70 33 33 90 12 9 17 13 73 31 36 91 17 15 13 15 54 36 32 92 16 12 15 13 77 35 41 93 15 12 14 15 82 32 28 94 13 12 13 10 80 29 30 95 16 10 16 11 80 39 36 96 16 13 12 16 69 37 35 97 16 9 14 11 78 35 31 98 16 12 17 11 81 37 34 99 14 10 15 10 76 32 36 100 16 14 17 10 76 38 36 101 16 11 12 16 73 37 35 102 20 15 16 12 85 36 37 103 15 11 11 11 66 32 28 104 16 11 15 16 79 33 39 105 13 12 9 19 68 40 32 106 17 12 16 11 76 38 35 107 16 12 15 16 71 41 39 108 16 11 10 15 54 36 35 109 12 7 10 24 46 43 42 110 16 12 15 14 82 30 34 111 16 14 11 15 74 31 33 112 17 11 13 11 88 32 41 113 13 11 14 15 38 32 33 114 12 10 18 12 76 37 34 115 18 13 16 10 86 37 32 116 14 13 14 14 54 33 40 117 14 8 14 13 70 34 40 118 13 11 14 9 69 33 35 119 16 12 14 15 90 38 36 120 13 11 12 15 54 33 37 121 16 13 14 14 76 31 27 122 13 12 15 11 89 38 39 123 16 14 15 8 76 37 38 124 15 13 15 11 73 33 31 125 16 15 13 11 79 31 33 126 15 10 17 8 90 39 32 127 17 11 17 10 74 44 39 128 15 9 19 11 81 33 36 129 12 11 15 13 72 35 33 130 16 10 13 11 71 32 33 131 10 11 9 20 66 28 32 132 16 8 15 10 77 40 37 133 12 11 15 15 65 27 30 134 14 12 15 12 74 37 38 135 15 12 16 14 82 32 29 136 13 9 11 23 54 28 22 137 15 11 14 14 63 34 35 138 11 10 11 16 54 30 35 139 12 8 15 11 64 35 34 140 8 9 13 12 69 31 35 141 16 8 15 10 54 32 34 142 15 9 16 14 84 30 34 143 17 15 14 12 86 30 35 144 16 11 15 12 77 31 23 145 10 8 16 11 89 40 31 146 18 13 16 12 76 32 27 147 13 12 11 13 60 36 36 148 16 12 12 11 75 32 31 149 13 9 9 19 73 35 32 150 10 7 16 12 85 38 39 151 15 13 13 17 79 42 37 152 16 9 16 9 71 34 38 153 16 6 12 12 72 35 39 154 14 8 9 19 69 35 34 155 10 8 13 18 78 33 31 156 17 15 13 15 54 36 32 157 13 6 14 14 69 32 37 158 15 9 19 11 81 33 36 159 16 11 13 9 84 34 32 160 12 8 12 18 84 32 35 161 13 8 13 16 69 34 36 162
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Learning software happiness depression belonging connected separate t_

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Fixed Seasonal Effects


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Degree of Predetermination (lagged endogenous variables)


Degree of Seasonal Predetermination


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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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) 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, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) 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,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) 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,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) 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,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) 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,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) 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
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