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Data X:
210907 30 112285 145 94 120982 28 84786 101 103 176508 38 83123 98 93 179321 30 101193 132 103 123185 22 38361 60 51 52746 26 68504 38 70 385534 25 119182 144 91 33170 18 22807 5 22 101645 11 17140 28 38 149061 26 116174 84 93 165446 25 57635 79 60 237213 38 66198 127 123 173326 44 71701 78 148 133131 30 57793 60 90 258873 40 80444 131 124 180083 34 53855 84 70 324799 47 97668 133 168 230964 30 133824 150 115 236785 31 101481 91 71 135473 23 99645 132 66 202925 36 114789 136 134 215147 36 99052 124 117 344297 30 67654 118 108 153935 25 65553 70 84 132943 39 97500 107 156 174724 34 69112 119 120 174415 31 82753 89 114 225548 31 85323 112 94 223632 33 72654 108 120 124817 25 30727 52 81 221698 33 77873 112 110 210767 35 117478 116 133 170266 42 74007 123 122 260561 43 90183 125 158 84853 30 61542 27 109 294424 33 101494 162 124 101011 13 27570 32 39 215641 32 55813 64 92 325107 36 79215 92 126 7176 0 1423 0 0 167542 28 55461 83 70 106408 14 31081 41 37 96560 17 22996 47 38 265769 32 83122 120 120 269651 30 70106 105 93 149112 35 60578 79 95 175824 20 39992 65 77 152871 28 79892 70 90 111665 28 49810 55 80 116408 39 71570 39 31 362301 34 100708 67 110 78800 26 33032 21 66 183167 39 82875 127 138 277965 39 139077 152 133 150629 33 71595 113 113 168809 28 72260 99 100 24188 4 5950 7 7 329267 39 115762 141 140 65029 18 32551 21 61 101097 14 31701 35 41 218946 29 80670 109 96 244052 44 143558 133 164 341570 21 117105 123 78 103597 16 23789 26 49 233328 28 120733 230 102 256462 35 105195 166 124 206161 28 73107 68 99 311473 38 132068 147 129 235800 23 149193 179 62 177939 36 46821 61 73 207176 32 87011 101 114 196553 29 95260 108 99 174184 25 55183 90 70 143246 27 106671 114 104 187559 36 73511 103 116 187681 28 92945 142 91 119016 23 78664 79 74 182192 40 70054 88 138 73566 23 22618 25 67 194979 40 74011 83 151 167488 28 83737 113 72 143756 34 69094 118 120 275541 33 93133 110 115 243199 28 95536 129 105 182999 34 225920 51 104 135649 30 62133 93 108 152299 33 61370 76 98 120221 22 43836 49 69 346485 38 106117 118 111 145790 26 38692 38 99 193339 35 84651 141 71 80953 8 56622 58 27 122774 24 15986 27 69 130585 29 95364 91 107 112611 20 26706 48 73 286468 29 89691 63 107 241066 45 67267 56 93 148446 37 126846 144 129 204713 33 41140 73 69 182079 33 102860 168 118 140344 25 51715 64 73 220516 32 55801 97 119 243060 29 111813 117 104 162765 28 120293 100 107 182613 28 138599 149 99 232138 31 161647 187 90 265318 52 115929 127 197 85574 21 24266 37 36 310839 24 162901 245 85 225060 41 109825 87 139 232317 33 129838 177 106 144966 32 37510 49 50 43287 19 43750 49 64 155754 20 40652 73 31 164709 31 87771 177 63 201940 31 85872 94 92 235454 32 89275 117 106 220801 18 44418 60 63 99466 23 192565 55 69 92661 17 35232 39 41 133328 20 40909 64 56 61361 12 13294 26 25 125930 17 32387 64 65 100750 30 140867 58 93 224549 31 120662 95 114 82316 10 21233 25 38 102010 13 44332 26 44 101523 22 61056 76 87 243511 42 101338 129 110 22938 1 1168 11 0 41566 9 13497 2 27 152474 32 65567 101 83 61857 11 25162 28 30 99923 25 32334 36 80 132487 36 40735 89 98 317394 31 91413 193 82 21054 0 855 4 0 209641 24 97068 84 60 22648 13 44339 23 28 31414 8 14116 39 9 46698 13 10288 14 33 131698 19 65622 78 59 91735 18 16563 14 49 244749 33 76643 101 115 184510 40 110681 82 140 79863 22 29011 24 49 128423 38 92696 36 120 97839 24 94785 75 66 38214 8 8773 16 21 151101 35 83209 55 124 272458 43 93815 131 152 172494 43 86687 131 139 108043 14 34553 39 38 328107 41 105547 144 144 250579 38 103487 139 120 351067 45 213688 211 160 158015 31 71220 78 114 98866 13 23517 50 39 85439 28 56926 39 78 229242 31 91721 90 119 351619 40 115168 166 141 84207 30 111194 12 101 120445 16 51009 57 56 324598 37 135777 133 133 131069 30 51513 69 83 204271 35 74163 119 116 165543 32 51633 119 90 141722 27 75345 65 36 116048 20 33416 61 50 250047 18 83305 49 61 299775 31 98952 101 97 195838 31 102372 196 98 173260 21 37238 15 78 254488 39 103772 136 117 104389 41 123969 89 148 136084 13 27142 40 41 199476 32 135400 123 105 92499 18 21399 21 55 224330 39 130115 163 132 135781 14 24874 29 44 74408 7 34988 35 21 81240 17 45549 13 50 14688 0 6023 5 0 181633 30 64466 96 73 271856 37 54990 151 86 7199 0 1644 6 0 46660 5 6179 13 13 17547 1 3926 3 4 133368 16 32755 56 57 95227 32 34777 23 48 152601 24 73224 57 46 98146 17 27114 14 48 79619 11 20760 43 32 59194 24 37636 20 68 139942 22 65461 72 87 118612 12 30080 87 43 72880 19 24094 21 67 65475 13 69008 56 46 99643 17 54968 59 46 71965 15 46090 82 56 77272 16 27507 43 48 49289 24 10672 25 44 135131 15 34029 38 60 108446 17 46300 25 65 89746 18 24760 38 55 44296 20 18779 12 38 77648 16 21280 29 52 181528 16 40662 47 60 134019 18 28987 45 54 124064 22 22827 40 86 92630 8 18513 30 24 121848 17 30594 41 52 52915 18 24006 25 49 81872 16 27913 23 61 58981 23 42744 14 61 53515 22 12934 16 81 60812 13 22574 26 43 56375 13 41385 21 40 65490 16 18653 27 40 80949 16 18472 9 56 76302 20 30976 33 68 104011 22 63339 42 79 98104 17 25568 68 47 67989 18 33747 32 57 30989 17 4154 6 41 135458 12 19474 67 29 73504 7 35130 33 3 63123 17 39067 77 60 61254 14 13310 46 30 74914 23 65892 30 79 31774 17 4143 0 47 81437 14 28579 36 40 87186 15 51776 46 48 50090 17 21152 18 36 65745 21 38084 48 42 56653 18 27717 29 49 158399 18 32928 28 57 46455 17 11342 34 12 73624 17 19499 33 40 38395 16 16380 34 43 91899 15 36874 33 33 139526 21 48259 80 77 52164 16 16734 32 43 51567 14 28207 30 45 70551 15 30143 41 47 84856 17 41369 41 43 102538 15 45833 51 45 86678 15 29156 18 50 85709 10 35944 34 35 34662 6 36278 31 7 150580 22 45588 39 71 99611 21 45097 54 67 19349 1 3895 14 0 99373 18 28394 24 62 86230 17 18632 24 54 30837 4 2325 8 4 31706 10 25139 26 25 89806 16 27975 19 40 62088 16 14483 11 38 40151 9 13127 14 19 27634 16 5839 1 17 76990 17 24069 39 67 37460 7 3738 5 14 54157 15 18625 37 30 49862 14 36341 32 54 84337 14 24548 38 35 64175 18 21792 47 59 59382 12 26263 47 24 119308 16 23686 37 58 76702 21 49303 51 42 103425 19 25659 45 46 70344 16 28904 21 61 43410 1 2781 1 3 104838 16 29236 42 52 62215 10 19546 26 25 69304 19 22818 21 40 53117 12 32689 4 32 19764 2 5752 10 4 86680 14 22197 43 49 84105 17 20055 34 63 77945 19 25272 31 67 89113 14 82206 19 32 91005 11 32073 34 23 40248 4 5444 6 7 64187 16 20154 11 54 50857 20 36944 24 37 56613 12 8019 16 35 62792 15 30884 72 51 72535 16 19540 21 39
Names of X columns:
time_in_rfc compendiums_reviewed totsize totblogs feedback_messages_p120
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
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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') }
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Raw Output
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