Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
0 13 0 96348 111716 110855 110933 97920 0 14 0 105425 96348 111716 110855 110933 0 15 0 114874 105425 96348 111716 110855 0 16 0 104199 114874 105425 96348 111716 0 17 0 101166 104199 114874 105425 96348 0 18 0 99010 101166 104199 114874 105425 0 19 0 101607 99010 101166 104199 114874 0 20 0 97492 101607 99010 101166 104199 0 21 0 106088 97492 101607 99010 101166 0 22 0 113536 106088 97492 101607 99010 0 23 0 112475 113536 106088 97492 101607 0 24 0 115491 112475 113536 106088 97492 0 25 0 97733 115491 112475 113536 106088 0 26 0 102591 97733 115491 112475 113536 0 27 0 114783 102591 97733 115491 112475 0 28 0 100397 114783 102591 97733 115491 0 29 0 97772 100397 114783 102591 97733 0 30 0 96128 97772 100397 114783 102591 0 31 0 91261 96128 97772 100397 114783 0 32 0 90686 91261 96128 97772 100397 0 33 0 97792 90686 91261 96128 97772 0 34 0 108848 97792 90686 91261 96128 0 35 0 109989 108848 97792 90686 91261 0 36 0 109453 109989 108848 97792 90686 0 37 0 93945 109453 109989 108848 97792 0 38 0 98750 93945 109453 109989 108848 0 39 0 119043 98750 93945 109453 109989 0 40 0 104776 119043 98750 93945 109453 0 41 0 103262 104776 119043 98750 93945 0 42 0 106735 103262 104776 119043 98750 0 43 0 101600 106735 103262 104776 119043 0 44 0 99358 101600 106735 103262 104776 0 45 0 105240 99358 101600 106735 103262 0 46 0 114079 105240 99358 101600 106735 0 47 0 121637 114079 105240 99358 101600 0 48 0 111747 121637 114079 105240 99358 0 49 0 99496 111747 121637 114079 105240 0 50 0 104992 99496 111747 121637 114079 0 51 0 124255 104992 99496 111747 121637 0 52 0 108258 124255 104992 99496 111747 0 53 0 106940 108258 124255 104992 99496 0 54 0 104939 106940 108258 124255 104992 0 55 0 105896 104939 106940 108258 124255 0 56 0 107287 105896 104939 106940 108258 0 57 0 110783 107287 105896 104939 106940 0 58 0 122139 110783 107287 105896 104939 0 59 0 125823 122139 110783 107287 105896 0 60 0 120480 125823 122139 110783 107287 0 61 0 103296 120480 125823 122139 110783 0 62 0 117121 103296 120480 125823 122139 0 63 0 129924 117121 103296 120480 125823 0 64 0 118589 129924 117121 103296 120480 0 65 0 118062 118589 129924 117121 103296 0 66 0 113597 118062 118589 129924 117121 0 67 0 117161 113597 118062 118589 129924 0 68 0 112893 117161 113597 118062 118589 0 69 0 119657 112893 117161 113597 118062 0 70 0 136562 119657 112893 117161 113597 0 71 0 140446 136562 119657 112893 117161 0 72 0 138744 140446 136562 119657 112893 0 73 0 120324 138744 140446 136562 119657 0 74 0 118113 120324 138744 140446 136562 0 75 0 130257 118113 120324 138744 140446 0 76 0 125510 130257 118113 120324 138744 0 77 0 117986 125510 130257 118113 120324 0 78 0 118316 117986 125510 130257 118113 0 79 0 122075 118316 117986 125510 130257 0 80 0 117573 122075 118316 117986 125510 0 81 0 122566 117573 122075 118316 117986 0 82 0 135934 122566 117573 122075 118316 0 83 0 138394 135934 122566 117573 122075 0 84 0 137999 138394 135934 122566 117573 0 85 0 118780 137999 138394 135934 122566 0 86 0 117907 118780 137999 138394 135934 0 87 0 142932 117907 118780 137999 138394 0 88 0 132200 142932 117907 118780 137999 0 89 0 125666 132200 142932 117907 118780 0 90 0 127958 125666 132200 142932 117907 0 91 0 127718 127958 125666 132200 142932 0 92 0 124368 127718 127958 125666 132200 0 93 0 135241 124368 127718 127958 125666 0 94 0 144734 135241 124368 127718 127958 0 95 0 142320 144734 135241 124368 127718 0 96 0 141481 142320 144734 135241 124368 0 97 0 120471 141481 142320 144734 135241 0 98 0 123422 120471 141481 142320 144734 0 99 0 145829 123422 120471 141481 142320 0 100 0 134572 145829 123422 120471 141481 0 101 0 132156 134572 145829 123422 120471 0 102 0 140265 132156 134572 145829 123422 0 103 0 137771 140265 132156 134572 145829 0 104 0 134035 137771 140265 132156 134572 0 105 0 144016 134035 137771 140265 132156 0 106 0 151905 144016 134035 137771 140265 0 107 0 155791 151905 144016 134035 137771 0 108 0 148440 155791 151905 144016 134035 0 109 0 129862 148440 155791 151905 144016 0 110 0 134264 129862 148440 155791 151905 0 111 0 151952 134264 129862 148440 155791 0 112 0 143191 151952 134264 129862 148440 0 113 0 137242 143191 151952 134264 129862 0 114 0 136993 137242 143191 151952 134264 0 115 0 134431 136993 137242 143191 151952 0 116 0 132523 134431 136993 137242 143191 0 117 0 133486 132523 134431 136993 137242 0 118 0 140120 133486 132523 134431 136993 1 119 119 137521 140120 133486 132523 134431 1 120 120 112193 137521 140120 133486 132523 1 121 121 94256 112193 137521 140120 133486 1 122 122 99047 94256 112193 137521 140120 1 123 123 109761 99047 94256 112193 137521 1 124 124 102160 109761 99047 94256 112193 1 125 125 104792 102160 109761 99047 94256 1 126 126 104341 104792 102160 109761 99047 1 127 127 112430 104341 104792 102160 109761 1 128 128 113034 112430 104341 104792 102160 1 129 129 114197 113034 112430 104341 104792 1 130 130 127876 114197 113034 112430 104341 1 131 131 135199 127876 114197 113034 112430 1 132 132 123663 135199 127876 114197 113034 1 133 133 112578 123663 135199 127876 114197 1 134 134 117104 112578 123663 135199 127876 1 135 135 139703 117104 112578 123663 135199 1 136 136 114961 139703 117104 112578 123663 1 137 137 134222 114961 139703 117104 112578 1 138 138 128390 134222 114961 139703 117104 1 139 139 134197 128390 134222 114961 139703 1 140 140 135963 134197 128390 134222 114961 1 141 141 135936 135963 134197 128390 134222 1 142 142 146803 135936 135963 134197 128390 1 143 143 143231 146803 135936 135963 134197 1 144 144 131510 143231 146803 135936 135963
Names of X columns:
crisis_10/8 t t_crisis_10/8 Totale_goederenvervoer_ton Totale_goederenvervoer_ton-1 Totale_goederenvervoer_ton-2 Totale_goederenvervoer_ton-3 Totale_goederenvervoer_ton-4
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Include Monthly 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$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') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation