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Data X:
210907 3 79 30 94 0 120982 4 58 28 103 0 176508 12 60 38 93 1 385534 0 121 25 91 0 149061 5 43 26 93 0 165446 0 69 25 60 1 237213 0 78 38 123 1 133131 7 44 30 90 1 324799 0 158 47 168 1 230964 4 102 30 115 0 236785 3 77 31 71 1 135473 0 82 23 66 0 215147 0 101 36 117 0 344297 1 80 30 108 1 153935 5 50 25 84 0 174724 0 123 34 120 1 174415 0 73 31 114 1 225548 5 81 31 94 0 223632 0 105 33 120 1 124817 0 47 25 81 1 210767 3 94 35 133 0 170266 4 44 42 122 0 294424 2 107 33 124 0 325107 0 84 36 126 1 7176 0 0 0 0 1 106408 1 33 14 37 0 96560 0 42 17 38 0 265769 2 96 32 120 1 149112 6 56 35 95 0 175824 0 57 20 77 1 152871 5 59 28 90 0 111665 4 39 28 80 1 362301 2 76 34 110 1 183167 0 91 39 138 0 168809 0 76 28 100 1 24188 0 8 4 7 1 329267 8 79 39 140 1 218946 1 76 29 96 1 244052 5 101 44 164 1 341570 1 94 21 78 1 103597 1 27 16 49 0 256462 0 123 35 124 1 235800 8 105 23 62 0 196553 2 41 29 99 1 174184 0 72 25 70 1 143246 5 67 27 104 0 187559 8 75 36 116 1 187681 2 114 28 91 0 73566 6 22 23 67 1 167488 2 69 28 72 0 143756 0 105 34 120 0 243199 3 88 28 105 0 182999 6 73 34 104 1 152299 0 62 33 98 1 346485 0 118 38 111 1 193339 2 100 35 71 1 122774 0 24 24 69 1 130585 5 67 29 107 0 112611 0 46 20 73 1 286468 1 57 29 107 1 148446 1 135 37 129 1 182079 2 124 33 118 0 140344 6 33 25 73 1 220516 1 98 32 119 1 243060 4 58 29 104 1 162765 2 68 28 107 1 232138 0 131 31 90 1 265318 10 110 52 197 0 85574 0 37 21 36 1 310839 9 130 24 85 0 225060 7 93 41 139 0 232317 0 118 33 106 1 144966 0 39 32 50 0 164709 0 81 31 63 1 220801 1 51 18 63 1 99466 0 28 23 69 0 92661 1 40 17 41 1 133328 0 56 20 56 1 61361 0 27 12 25 1 100750 0 83 30 93 1 102010 3 28 13 44 0 101523 0 59 22 87 1 243511 0 133 42 110 1 22938 0 12 1 0 1 152474 0 106 32 83 1 99923 0 44 25 80 0 132487 0 71 36 98 1 317394 1 116 31 82 0 21054 0 4 0 0 1 209641 5 62 24 60 1 22648 0 12 13 28 0 31414 0 18 8 9 0 46698 0 14 13 33 1 131698 0 60 19 59 1 244749 2 98 33 115 1 128423 8 32 38 120 0 97839 2 25 24 66 0 272458 0 100 43 152 1 108043 1 45 14 38 1 328107 3 129 41 144 0 351067 3 136 45 160 1 158015 0 59 31 114 0 229242 4 63 31 119 1 84207 11 14 30 101 1 120445 0 36 16 56 0 324598 0 113 37 133 0 131069 4 47 30 83 0 204271 0 92 35 116 0 116048 0 50 20 50 0 250047 0 41 18 61 1 299775 9 91 31 97 1 195838 1 111 31 98 0 173260 3 41 21 78 1 254488 10 120 39 117 0 92499 0 25 18 55 1 224330 1 131 39 132 0 135781 2 45 14 44 0 74408 4 29 7 21 1 81240 0 58 17 50 0 181633 2 47 30 73 1 271856 1 109 37 86 1 95227 0 37 32 48 1 98146 0 15 17 48 0 59194 6 7 24 68 0 139942 0 54 22 87 1 118612 2 54 12 43 0 72880 0 14 19 67 1 65475 2 16 13 46 1 71965 1 32 15 56 1 135131 0 38 15 60 0 108446 1 22 17 65 0 181528 0 32 16 60 1 134019 0 32 18 54 1 121848 0 37 17 52 0 81872 0 32 16 61 0 58981 7 0 23 61 0 53515 2 5 22 81 0 56375 7 10 13 40 1 65490 3 27 16 40 1 76302 0 29 20 68 1 104011 6 25 22 79 1 98104 2 55 17 47 0 30989 0 5 17 41 1 135458 3 43 12 29 0 63123 1 34 17 60 1 74914 0 35 23 79 1 31774 1 0 17 47 0 81437 0 37 14 40 1 65745 0 26 21 42 1 56653 0 38 18 49 1 158399 0 23 18 57 1 73624 0 30 17 40 1 91899 0 18 15 33 1 139526 0 28 21 77 1 51567 2 21 14 45 0 102538 1 50 15 45 0 86678 0 12 15 50 1 150580 0 27 22 71 1 99611 0 41 21 67 1 99373 1 12 18 62 0 86230 0 21 17 54 0 30837 0 8 4 4 0 31706 0 26 10 25 1 89806 0 27 16 40 1 64175 0 37 18 59 0 59382 0 29 12 24 0 119308 0 32 16 58 0 76702 0 35 21 42 0 19764 1 10 2 4 1 84105 0 17 17 63 0 64187 0 10 16 54 1 72535 0 17 16 39 1
Names of X columns:
time_in_rfc shared_compendiums blogged_computations compendiums_reviewed feedback_messages_p120 What_is_your_gender?
Sample Range:
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From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal 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') }
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Raw Output
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Computing time
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R Server
Big Analytics Cloud Computing Center
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