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Data X:
Yt XT XT-1 Xt-2 XT-3 XT-4 100 100 91.12460521 94.04971502 97.82226485 100 97.82226485 99.87129987 93.13202153 91.12460521 94.04971502 97.82226485 94.04971502 99.54459954 93.88342812 93.13202153 91.12460521 94.04971502 91.12460521 99.81189981 92.55349954 93.88342812 93.13202153 91.12460521 93.13202153 100.4851005 94.43494835 92.55349954 93.88342812 93.13202153 93.88342812 101.1385011 96.25017563 94.43494835 92.55349954 93.88342812 92.55349954 101.3662014 100.4355715 96.25017563 94.43494835 92.55349954 94.43494835 101.5147015 101.5036685 100.4355715 96.25017563 94.43494835 96.25017563 101.8216018 99.39789728 101.5036685 100.4355715 96.25017563 100.4355715 102.4354024 99.68990733 99.39789728 101.5036685 100.4355715 101.5036685 102.5344025 101.6895041 99.68990733 99.39789728 101.5036685 99.39789728 102.6532027 103.6652759 101.6895041 99.68990733 99.39789728 99.68990733 102.4651025 103.0532766 103.6652759 101.6895041 99.68990733 101.6895041 102.4354024 100.9500712 103.0532766 103.6652759 101.6895041 103.6652759 102.4156024 102.345366 100.9500712 103.0532766 103.6652759 103.0532766 102.4453024 101.6472299 102.345366 100.9500712 103.0532766 100.9500712 102.8908029 99.56809393 101.6472299 102.345366 100.9500712 102.345366 102.8512029 95.67727392 99.56809393 101.6472299 102.345366 101.6472299 103.3561034 96.58494865 95.67727392 99.56809393 101.6472299 99.56809393 103.7422037 96.32604937 96.58494865 95.67727392 99.56809393 95.67727392 103.7224037 95.37109101 96.32604937 96.58494865 95.67727392 96.58494865 104.0788041 96.00056203 95.37109101 96.32604937 96.58494865 96.32604937 104.2075042 96.88367859 96.00056203 95.37109101 96.32604937 95.37109101 103.9105039 94.85280372 96.88367859 96.00056203 95.37109101 96.00056203 103.7026037 92.46943974 94.85280372 96.88367859 96.00056203 96.88367859 103.960004 93.99180173 92.46943974 94.85280372 96.88367859 94.85280372 104.0986041 93.45262168 93.99180173 92.46943974 94.85280372 92.46943974 104.1481041 92.26698759 93.45262168 93.99180173 92.46943974 93.99180173 104.7124047 90.39653498 92.26698759 93.45262168 93.99180173 93.45262168 104.7223047 90.43001228 90.39653498 92.26698759 93.45262168 92.26698759 105.1975052 91.04995327 90.43001228 90.39653498 92.26698759 90.39653498 105.0688051 89.07845784 91.04995327 90.43001228 90.39653498 90.43001228 105.0589051 89.69314509 89.07845784 91.04995327 90.43001228 91.04995327 105.5044055 87.92459054 89.69314509 89.07845784 91.04995327 89.07845784 105.3757054 85.8789319 87.92459054 89.69314509 89.07845784 89.69314509 105.4747055 83.20612366 85.8789319 87.92459054 89.69314509 87.92459054 106.029106 83.85722053 83.20612366 85.8789319 87.92459054 85.8789319 107.019107 83.01393462 83.85722053 83.20612366 85.8789319 83.20612366 107.3161073 82.84508195 83.01393462 83.85722053 83.20612366 83.85722053 107.7517078 78.68864276 82.84508195 83.01393462 83.85722053 83.01393462 108.5239085 77.56959675 78.68864276 82.84508195 83.01393462 82.84508195 109.3159093 78.53689529 77.56959675 78.68864276 82.84508195 78.68864276 109.5634096 78.55717715 78.53689529 77.56959675 78.68864276 77.56959675 110.5435105 77.4761291 78.55717715 78.53689529 77.56959675 78.53689529 111.1573112 81.58931659 77.4761291 78.55717715 78.53689529 78.55717715 111.7414117 85.02428326 81.58931659 77.4761291 78.55717715 77.4761291 111.0583111 91.71290159 85.02428326 81.58931659 77.4761291 81.58931659 111.2365112 95.96293061 91.71290159 85.02428326 81.58931659 85.02428326 111.038511 90.84689022 95.96293061 91.71290159 85.02428326 91.71290159 110.3752104 92.28788036 90.84689022 95.96293061 91.71290159 95.96293061 110.1376101 95.56511274 92.28788036 90.84689022 95.96293061 90.84689022 110.2465102 93.62452884 95.56511274 92.28788036 90.84689022 92.28788036 110.6227106 92.63071726 93.62452884 95.56511274 92.28788036 95.56511274 109.98911 89.50914211 92.63071726 93.62452884 95.56511274 93.62452884 110.2168102 87.17171779 89.50914211 92.63071726 93.62452884 92.63071726 110.1376101 86.72624975 87.17171779 89.50914211 92.63071726 89.50914211 109.9297099 85.63212844 86.72624975 87.17171779 89.50914211
Names of X columns:
wisselkoers consumptieprijzen jkljlkj jkljljk jkjklkl jkljkl

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
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') }

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
R Engine error message	Error in array(list(Yt, XT, XT - 1, Xt - 2, XT - 3, XT - 4, 100, 100, : object 'Yt' not found Execution halted