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Data X:
2849.27 10872 2756.76 2645.64 2497.84 2448.05 2454.62 2407.6 2472.81 2408.64 2440.25 2350.44 2196.72 2174.56 2921.44 10625 2849.27 2756.76 2645.64 2497.84 2448.05 2454.62 2407.6 2472.81 2408.64 2440.25 2350.44 2196.72 2981.85 10407 2921.44 2849.27 2756.76 2645.64 2497.84 2448.05 2454.62 2407.6 2472.81 2408.64 2440.25 2350.44 3080.58 10463 2981.85 2921.44 2849.27 2756.76 2645.64 2497.84 2448.05 2454.62 2407.6 2472.81 2408.64 2440.25 3106.22 10556 3080.58 2981.85 2921.44 2849.27 2756.76 2645.64 2497.84 2448.05 2454.62 2407.6 2472.81 2408.64 3119.31 10646 3106.22 3080.58 2981.85 2921.44 2849.27 2756.76 2645.64 2497.84 2448.05 2454.62 2407.6 2472.81 3061.26 10702 3119.31 3106.22 3080.58 2981.85 2921.44 2849.27 2756.76 2645.64 2497.84 2448.05 2454.62 2407.6 3097.31 11353 3061.26 3119.31 3106.22 3080.58 2981.85 2921.44 2849.27 2756.76 2645.64 2497.84 2448.05 2454.62 3161.69 11346 3097.31 3061.26 3119.31 3106.22 3080.58 2981.85 2921.44 2849.27 2756.76 2645.64 2497.84 2448.05 3257.16 11451 3161.69 3097.31 3061.26 3119.31 3106.22 3080.58 2981.85 2921.44 2849.27 2756.76 2645.64 2497.84 3277.01 11964 3257.16 3161.69 3097.31 3061.26 3119.31 3106.22 3080.58 2981.85 2921.44 2849.27 2756.76 2645.64 3295.32 12574 3277.01 3257.16 3161.69 3097.31 3061.26 3119.31 3106.22 3080.58 2981.85 2921.44 2849.27 2756.76 3363.99 13031 3295.32 3277.01 3257.16 3161.69 3097.31 3061.26 3119.31 3106.22 3080.58 2981.85 2921.44 2849.27 3494.17 13812 3363.99 3295.32 3277.01 3257.16 3161.69 3097.31 3061.26 3119.31 3106.22 3080.58 2981.85 2921.44 3667.03 14544 3494.17 3363.99 3295.32 3277.01 3257.16 3161.69 3097.31 3061.26 3119.31 3106.22 3080.58 2981.85 3813.06 14931 3667.03 3494.17 3363.99 3295.32 3277.01 3257.16 3161.69 3097.31 3061.26 3119.31 3106.22 3080.58 3917.96 14886 3813.06 3667.03 3494.17 3363.99 3295.32 3277.01 3257.16 3161.69 3097.31 3061.26 3119.31 3106.22 3895.51 16005 3917.96 3813.06 3667.03 3494.17 3363.99 3295.32 3277.01 3257.16 3161.69 3097.31 3061.26 3119.31 3801.06 17064 3895.51 3917.96 3813.06 3667.03 3494.17 3363.99 3295.32 3277.01 3257.16 3161.69 3097.31 3061.26 3570.12 15168 3801.06 3895.51 3917.96 3813.06 3667.03 3494.17 3363.99 3295.32 3277.01 3257.16 3161.69 3097.31 3701.61 16050 3570.12 3801.06 3895.51 3917.96 3813.06 3667.03 3494.17 3363.99 3295.32 3277.01 3257.16 3161.69 3862.27 15839 3701.61 3570.12 3801.06 3895.51 3917.96 3813.06 3667.03 3494.17 3363.99 3295.32 3277.01 3257.16 3970.1 15137 3862.27 3701.61 3570.12 3801.06 3895.51 3917.96 3813.06 3667.03 3494.17 3363.99 3295.32 3277.01 4138.52 14954 3970.1 3862.27 3701.61 3570.12 3801.06 3895.51 3917.96 3813.06 3667.03 3494.17 3363.99 3295.32 4199.75 15648 4138.52 3970.1 3862.27 3701.61 3570.12 3801.06 3895.51 3917.96 3813.06 3667.03 3494.17 3363.99 4290.89 15305 4199.75 4138.52 3970.1 3862.27 3701.61 3570.12 3801.06 3895.51 3917.96 3813.06 3667.03 3494.17 4443.91 15579 4290.89 4199.75 4138.52 3970.1 3862.27 3701.61 3570.12 3801.06 3895.51 3917.96 3813.06 3667.03 4502.64 16348 4443.91 4290.89 4199.75 4138.52 3970.1 3862.27 3701.61 3570.12 3801.06 3895.51 3917.96 3813.06 4356.98 15928 4502.64 4443.91 4290.89 4199.75 4138.52 3970.1 3862.27 3701.61 3570.12 3801.06 3895.51 3917.96 4591.27 16171 4356.98 4502.64 4443.91 4290.89 4199.75 4138.52 3970.1 3862.27 3701.61 3570.12 3801.06 3895.51 4696.96 15937 4591.27 4356.98 4502.64 4443.91 4290.89 4199.75 4138.52 3970.1 3862.27 3701.61 3570.12 3801.06 4621.4 15713 4696.96 4591.27 4356.98 4502.64 4443.91 4290.89 4199.75 4138.52 3970.1 3862.27 3701.61 3570.12 4562.84 15594 4621.4 4696.96 4591.27 4356.98 4502.64 4443.91 4290.89 4199.75 4138.52 3970.1 3862.27 3701.61 4202.52 15683 4562.84 4621.4 4696.96 4591.27 4356.98 4502.64 4443.91 4290.89 4199.75 4138.52 3970.1 3862.27 4296.49 16438 4202.52 4562.84 4621.4 4696.96 4591.27 4356.98 4502.64 4443.91 4290.89 4199.75 4138.52 3970.1 4435.23 17032 4296.49 4202.52 4562.84 4621.4 4696.96 4591.27 4356.98 4502.64 4443.91 4290.89 4199.75 4138.52 4105.18 17696 4435.23 4296.49 4202.52 4562.84 4621.4 4696.96 4591.27 4356.98 4502.64 4443.91 4290.89 4199.75 4116.68 17745 4105.18 4435.23 4296.49 4202.52 4562.84 4621.4 4696.96 4591.27 4356.98 4502.64 4443.91 4290.89 3844.49 19394 4116.68 4105.18 4435.23 4296.49 4202.52 4562.84 4621.4 4696.96 4591.27 4356.98 4502.64 4443.91 3720.98 20148 3844.49 4116.68 4105.18 4435.23 4296.49 4202.52 4562.84 4621.4 4696.96 4591.27 4356.98 4502.64 3674.4 20108 3720.98 3844.49 4116.68 4105.18 4435.23 4296.49 4202.52 4562.84 4621.4 4696.96 4591.27 4356.98 3857.62 18584 3674.4 3720.98 3844.49 4116.68 4105.18 4435.23 4296.49 4202.52 4562.84 4621.4 4696.96 4591.27 3801.06 18441 3857.62 3674.4 3720.98 3844.49 4116.68 4105.18 4435.23 4296.49 4202.52 4562.84 4621.4 4696.96 3504.37 18391 3801.06 3857.62 3674.4 3720.98 3844.49 4116.68 4105.18 4435.23 4296.49 4202.52 4562.84 4621.4 3032.6 19178 3504.37 3801.06 3857.62 3674.4 3720.98 3844.49 4116.68 4105.18 4435.23 4296.49 4202.52 4562.84 3047.03 18079 3032.6 3504.37 3801.06 3857.62 3674.4 3720.98 3844.49 4116.68 4105.18 4435.23 4296.49 4202.52 2962.34 18483 3047.03 3032.6 3504.37 3801.06 3857.62 3674.4 3720.98 3844.49 4116.68 4105.18 4435.23 4296.49 2197.82 19644 2962.34 3047.03 3032.6 3504.37 3801.06 3857.62 3674.4 3720.98 3844.49 4116.68 4105.18 4435.23 2014.45 19195 2197.82 2962.34 3047.03 3032.6 3504.37 3801.06 3857.62 3674.4 3720.98 3844.49 4116.68 4105.18 1862.83 19650 2014.45 2197.82 2962.34 3047.03 3032.6 3504.37 3801.06 3857.62 3674.4 3720.98 3844.49 4116.68 1905.41 20830 1862.83 2014.45 2197.82 2962.34 3047.03 3032.6 3504.37 3801.06 3857.62 3674.4 3720.98 3844.49 1810.99 23595 1905.41 1862.83 2014.45 2197.82 2962.34 3047.03 3032.6 3504.37 3801.06 3857.62 3674.4 3720.98 1670.07 22937 1810.99 1905.41 1862.83 2014.45 2197.82 2962.34 3047.03 3032.6 3504.37 3801.06 3857.62 3674.4 1864.44 21814 1670.07 1810.99 1905.41 1862.83 2014.45 2197.82 2962.34 3047.03 3032.6 3504.37 3801.06 3857.62 2052.02 21928 1864.44 1670.07 1810.99 1905.41 1862.83 2014.45 2197.82 2962.34 3047.03 3032.6 3504.37 3801.06 2029.6 21777 2052.02 1864.44 1670.07 1810.99 1905.41 1862.83 2014.45 2197.82 2962.34 3047.03 3032.6 3504.37 2070.83 21383 2029.6 2052.02 1864.44 1670.07 1810.99 1905.41 1862.83 2014.45 2197.82 2962.34 3047.03 3032.6 2293.41 21467 2070.83 2029.6 2052.02 1864.44 1670.07 1810.99 1905.41 1862.83 2014.45 2197.82 2962.34 3047.03 2443.27 22052 2293.41 2070.83 2029.6 2052.02 1864.44 1670.07 1810.99 1905.41 1862.83 2014.45 2197.82 2962.34 2513.17 22680 2443.27 2293.41 2070.83 2029.6 2052.02 1864.44 1670.07 1810.99 1905.41 1862.83 2014.45 2197.82
Names of X columns:
Y X Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12
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
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
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2
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4
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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
1 seconds
R Server
Big Analytics Cloud Computing Center
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