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Data X:
210907 56 3 79 94 120982 56 4 58 103 176508 54 12 60 93 179321 89 2 108 103 123185 40 1 49 51 52746 25 3 0 70 385534 92 0 121 91 33170 18 0 1 22 101645 63 0 20 38 149061 44 5 43 93 165446 33 0 69 60 237213 84 0 78 123 173326 88 7 86 148 133131 55 7 44 90 258873 60 3 104 124 180083 66 9 63 70 324799 154 0 158 168 230964 53 4 102 115 236785 119 3 77 71 135473 41 0 82 66 202925 61 7 115 134 215147 58 0 101 117 344297 75 1 80 108 153935 33 5 50 84 132943 40 7 83 156 174724 92 0 123 120 174415 100 0 73 114 225548 112 5 81 94 223632 73 0 105 120 124817 40 0 47 81 221698 45 0 105 110 210767 60 3 94 133 170266 62 4 44 122 260561 75 1 114 158 84853 31 4 38 109 294424 77 2 107 124 101011 34 0 30 39 215641 46 0 71 92 325107 99 0 84 126 7176 17 0 0 0 167542 66 2 59 70 106408 30 1 33 37 96560 76 0 42 38 265769 146 2 96 120 269651 67 10 106 93 149112 56 6 56 95 175824 107 0 57 77 152871 58 5 59 90 111665 34 4 39 80 116408 61 1 34 31 362301 119 2 76 110 78800 42 2 20 66 183167 66 0 91 138 277965 89 8 115 133 150629 44 3 85 113 168809 66 0 76 100 24188 24 0 8 7 329267 259 8 79 140 65029 17 5 21 61 101097 64 3 30 41 218946 41 1 76 96 244052 68 5 101 164 341570 168 1 94 78 103597 43 1 27 49 233328 132 5 92 102 256462 105 0 123 124 206161 71 12 75 99 311473 112 8 128 129 235800 94 8 105 62 177939 82 8 55 73 207176 70 8 56 114 196553 57 2 41 99 174184 53 0 72 70 143246 103 5 67 104 187559 121 8 75 116 187681 62 2 114 91 119016 52 5 118 74 182192 52 12 77 138 73566 32 6 22 67 194979 62 7 66 151 167488 45 2 69 72 143756 46 0 105 120 275541 63 4 116 115 243199 75 3 88 105 182999 88 6 73 104 135649 46 2 99 108 152299 53 0 62 98 120221 37 1 53 69 346485 90 0 118 111 145790 63 5 30 99 193339 78 2 100 71 80953 25 0 49 27 122774 45 0 24 69 130585 46 5 67 107 112611 41 0 46 73 286468 144 1 57 107 241066 82 0 75 93 148446 91 1 135 129 204713 71 1 68 69 182079 63 2 124 118 140344 53 6 33 73 220516 62 1 98 119 243060 63 4 58 104 162765 32 2 68 107 182613 39 3 81 99 232138 62 0 131 90 265318 117 10 110 197 85574 34 0 37 36 310839 92 9 130 85 225060 93 7 93 139 232317 54 0 118 106 144966 144 0 39 50 43287 14 4 13 64 155754 61 4 74 31 164709 109 0 81 63 201940 38 0 109 92 235454 73 0 151 106 220801 75 1 51 63 99466 50 0 28 69 92661 61 1 40 41 133328 55 0 56 56 61361 77 0 27 25 125930 75 4 37 65 100750 72 0 83 93 224549 50 4 54 114 82316 32 4 27 38 102010 53 3 28 44 101523 42 0 59 87 243511 71 0 133 110 22938 10 0 12 0 41566 35 5 0 27 152474 65 0 106 83 61857 25 4 23 30 99923 66 0 44 80 132487 41 0 71 98 317394 86 1 116 82 21054 16 0 4 0 209641 42 5 62 60 22648 19 0 12 28 31414 19 0 18 9 46698 45 0 14 33 131698 65 0 60 59 91735 35 0 7 49 244749 95 2 98 115 184510 49 7 64 140 79863 37 1 29 49 128423 64 8 32 120 97839 38 2 25 66 38214 34 0 16 21 151101 32 2 48 124 272458 65 0 100 152 172494 52 0 46 139 108043 62 1 45 38 328107 65 3 129 144 250579 83 0 130 120 351067 95 3 136 160 158015 29 0 59 114 98866 18 0 25 39 85439 33 0 32 78 229242 247 4 63 119 351619 139 4 95 141 84207 29 11 14 101 120445 118 0 36 56 324598 110 0 113 133 131069 67 4 47 83 204271 42 0 92 116 165543 65 1 70 90 141722 94 0 19 36 116048 64 0 50 50 250047 81 0 41 61 299775 95 9 91 97 195838 67 1 111 98 173260 63 3 41 78 254488 83 10 120 117 104389 45 5 135 148 136084 30 0 27 41 199476 70 2 87 105 92499 32 0 25 55 224330 83 1 131 132 135781 31 2 45 44 74408 67 4 29 21 81240 66 0 58 50 14688 10 0 4 0 181633 70 2 47 73 271856 103 1 109 86 7199 5 0 7 0 46660 20 0 12 13 17547 5 0 0 4 133368 36 1 37 57 95227 34 0 37 48 152601 48 2 46 46 98146 40 0 15 48 79619 43 3 42 32 59194 31 6 7 68 139942 42 0 54 87 118612 46 2 54 43 72880 33 0 14 67 65475 18 2 16 46 99643 55 1 33 46 71965 35 1 32 56 77272 59 2 21 48 49289 19 1 15 44 135131 66 0 38 60 108446 60 1 22 65 89746 36 3 28 55 44296 25 0 10 38 77648 47 0 31 52 181528 54 0 32 60 134019 53 0 32 54 124064 40 1 43 86 92630 40 4 27 24 121848 39 0 37 52 52915 14 0 20 49 81872 45 0 32 61 58981 36 7 0 61 53515 28 2 5 81 60812 44 0 26 43 56375 30 7 10 40 65490 22 3 27 40 80949 17 0 11 56 76302 31 0 29 68 104011 55 6 25 79 98104 54 2 55 47 67989 21 0 23 57 30989 14 0 5 41 135458 81 3 43 29 73504 35 0 23 3 63123 43 1 34 60 61254 46 1 36 30 74914 30 0 35 79 31774 23 1 0 47 81437 38 0 37 40 87186 54 0 28 48 50090 20 0 16 36 65745 53 0 26 42 56653 45 0 38 49 158399 39 0 23 57 46455 20 0 22 12 73624 24 0 30 40 38395 31 0 16 43 91899 35 0 18 33 139526 151 0 28 77 52164 52 0 32 43 51567 30 2 21 45 70551 31 0 23 47 84856 29 1 29 43 102538 57 1 50 45 86678 40 0 12 50 85709 44 0 21 35 34662 25 0 18 7 150580 77 0 27 71 99611 35 0 41 67 19349 11 0 13 0 99373 63 1 12 62 86230 44 0 21 54 30837 19 0 8 4 31706 13 0 26 25 89806 42 0 27 40 62088 38 1 13 38 40151 29 0 16 19 27634 20 0 2 17 76990 27 0 42 67 37460 20 0 5 14 54157 19 0 37 30 49862 37 0 17 54 84337 26 0 38 35 64175 42 0 37 59 59382 49 0 29 24 119308 30 0 32 58 76702 49 0 35 42 103425 67 1 17 46 70344 28 0 20 61 43410 19 0 7 3 104838 49 1 46 52 62215 27 0 24 25 69304 30 6 40 40 53117 22 3 3 32 19764 12 1 10 4 86680 31 2 37 49 84105 20 0 17 63 77945 20 0 28 67 89113 39 0 19 32 91005 29 3 29 23 40248 16 1 8 7 64187 27 0 10 54 50857 21 0 15 37 56613 19 1 15 35 62792 35 0 28 51 72535 14 0 17 39
Names of X columns:
Tijd_RFC #Logins #Gedeelde_Compendia #Blogs #Reviews+120tekens
Sample Range:
(leave blank to include all observations)
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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