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Data X:
210907 79 30 3 112285 120982 58 28 4 84786 176508 60 38 12 83123 179321 108 30 2 101193 123185 49 22 1 38361 52746 0 26 3 68504 385534 121 25 0 119182 33170 1 18 0 22807 101645 20 11 0 17140 149061 43 26 5 116174 165446 69 25 0 57635 237213 78 38 0 66198 173326 86 44 7 71701 133131 44 30 7 57793 258873 104 40 3 80444 180083 63 34 9 53855 324799 158 47 0 97668 230964 102 30 4 133824 236785 77 31 3 101481 135473 82 23 0 99645 202925 115 36 7 114789 215147 101 36 0 99052 344297 80 30 1 67654 153935 50 25 5 65553 132943 83 39 7 97500 174724 123 34 0 69112 174415 73 31 0 82753 225548 81 31 5 85323 223632 105 33 0 72654 124817 47 25 0 30727 221698 105 33 0 77873 210767 94 35 3 117478 170266 44 42 4 74007 260561 114 43 1 90183 84853 38 30 4 61542 294424 107 33 2 101494 101011 30 13 0 27570 215641 71 32 0 55813 325107 84 36 0 79215 7176 0 0 0 1423 167542 59 28 2 55461 106408 33 14 1 31081 96560 42 17 0 22996 265769 96 32 2 83122 269651 106 30 10 70106 149112 56 35 6 60578 175824 57 20 0 39992 152871 59 28 5 79892 111665 39 28 4 49810 116408 34 39 1 71570 362301 76 34 2 100708 78800 20 26 2 33032 183167 91 39 0 82875 277965 115 39 8 139077 150629 85 33 3 71595 168809 76 28 0 72260 24188 8 4 0 5950 329267 79 39 8 115762 65029 21 18 5 32551 101097 30 14 3 31701 218946 76 29 1 80670 244052 101 44 5 143558 341570 94 21 1 117105 103597 27 16 1 23789 233328 92 28 5 120733 256462 123 35 0 105195 206161 75 28 12 73107 311473 128 38 8 132068 235800 105 23 8 149193 177939 55 36 8 46821 207176 56 32 8 87011 196553 41 29 2 95260 174184 72 25 0 55183 143246 67 27 5 106671 187559 75 36 8 73511 187681 114 28 2 92945 119016 118 23 5 78664 182192 77 40 12 70054 73566 22 23 6 22618 194979 66 40 7 74011 167488 69 28 2 83737 143756 105 34 0 69094 275541 116 33 4 93133 243199 88 28 3 95536 182999 73 34 6 225920 135649 99 30 2 62133 152299 62 33 0 61370 120221 53 22 1 43836 346485 118 38 0 106117 145790 30 26 5 38692 193339 100 35 2 84651 80953 49 8 0 56622 122774 24 24 0 15986 130585 67 29 5 95364 112611 46 20 0 26706 286468 57 29 1 89691 241066 75 45 0 67267 148446 135 37 1 126846 204713 68 33 1 41140 182079 124 33 2 102860 140344 33 25 6 51715 220516 98 32 1 55801 243060 58 29 4 111813 162765 68 28 2 120293 182613 81 28 3 138599 232138 131 31 0 161647 265318 110 52 10 115929 85574 37 21 0 24266 310839 130 24 9 162901 225060 93 41 7 109825 232317 118 33 0 129838 144966 39 32 0 37510 43287 13 19 4 43750 155754 74 20 4 40652 164709 81 31 0 87771 201940 109 31 0 85872 235454 151 32 0 89275 220801 51 18 1 44418 99466 28 23 0 192565 92661 40 17 1 35232 133328 56 20 0 40909 61361 27 12 0 13294 125930 37 17 4 32387 100750 83 30 0 140867 224549 54 31 4 120662 82316 27 10 4 21233 102010 28 13 3 44332 101523 59 22 0 61056 243511 133 42 0 101338 22938 12 1 0 1168 41566 0 9 5 13497 152474 106 32 0 65567 61857 23 11 4 25162 99923 44 25 0 32334 132487 71 36 0 40735 317394 116 31 1 91413 21054 4 0 0 855 209641 62 24 5 97068 22648 12 13 0 44339 31414 18 8 0 14116 46698 14 13 0 10288 131698 60 19 0 65622 91735 7 18 0 16563 244749 98 33 2 76643 184510 64 40 7 110681 79863 29 22 1 29011 128423 32 38 8 92696 97839 25 24 2 94785 38214 16 8 0 8773 151101 48 35 2 83209 272458 100 43 0 93815 172494 46 43 0 86687 108043 45 14 1 34553 328107 129 41 3 105547 250579 130 38 0 103487 351067 136 45 3 213688 158015 59 31 0 71220 98866 25 13 0 23517 85439 32 28 0 56926 229242 63 31 4 91721 351619 95 40 4 115168 84207 14 30 11 111194 120445 36 16 0 51009 324598 113 37 0 135777 131069 47 30 4 51513 204271 92 35 0 74163 165543 70 32 1 51633 141722 19 27 0 75345 116048 50 20 0 33416 250047 41 18 0 83305 299775 91 31 9 98952 195838 111 31 1 102372 173260 41 21 3 37238 254488 120 39 10 103772 104389 135 41 5 123969 136084 27 13 0 27142 199476 87 32 2 135400 92499 25 18 0 21399 224330 131 39 1 130115 135781 45 14 2 24874 74408 29 7 4 34988 81240 58 17 0 45549 14688 4 0 0 6023 181633 47 30 2 64466 271856 109 37 1 54990 7199 7 0 0 1644 46660 12 5 0 6179 17547 0 1 0 3926 133368 37 16 1 32755 95227 37 32 0 34777 152601 46 24 2 73224 98146 15 17 0 27114 79619 42 11 3 20760 59194 7 24 6 37636 139942 54 22 0 65461 118612 54 12 2 30080 72880 14 19 0 24094 65475 16 13 2 69008 99643 33 17 1 54968 71965 32 15 1 46090 77272 21 16 2 27507 49289 15 24 1 10672 135131 38 15 0 34029 108446 22 17 1 46300 89746 28 18 3 24760 44296 10 20 0 18779 77648 31 16 0 21280 181528 32 16 0 40662 134019 32 18 0 28987 124064 43 22 1 22827 92630 27 8 4 18513 121848 37 17 0 30594 52915 20 18 0 24006 81872 32 16 0 27913 58981 0 23 7 42744 53515 5 22 2 12934 60812 26 13 0 22574 56375 10 13 7 41385 65490 27 16 3 18653 80949 11 16 0 18472 76302 29 20 0 30976 104011 25 22 6 63339 98104 55 17 2 25568 67989 23 18 0 33747 30989 5 17 0 4154 135458 43 12 3 19474 73504 23 7 0 35130 63123 34 17 1 39067 61254 36 14 1 13310 74914 35 23 0 65892 31774 0 17 1 4143 81437 37 14 0 28579 87186 28 15 0 51776 50090 16 17 0 21152 65745 26 21 0 38084 56653 38 18 0 27717 158399 23 18 0 32928 46455 22 17 0 11342 73624 30 17 0 19499 38395 16 16 0 16380 91899 18 15 0 36874 139526 28 21 0 48259 52164 32 16 0 16734 51567 21 14 2 28207 70551 23 15 0 30143 84856 29 17 1 41369 102538 50 15 1 45833 86678 12 15 0 29156 85709 21 10 0 35944 34662 18 6 0 36278 150580 27 22 0 45588 99611 41 21 0 45097 19349 13 1 0 3895 99373 12 18 1 28394 86230 21 17 0 18632 30837 8 4 0 2325 31706 26 10 0 25139 89806 27 16 0 27975 62088 13 16 1 14483 40151 16 9 0 13127 27634 2 16 0 5839 76990 42 17 0 24069 37460 5 7 0 3738 54157 37 15 0 18625 49862 17 14 0 36341 84337 38 14 0 24548 64175 37 18 0 21792 59382 29 12 0 26263 119308 32 16 0 23686 76702 35 21 0 49303 103425 17 19 1 25659 70344 20 16 0 28904 43410 7 1 0 2781 104838 46 16 1 29236 62215 24 10 0 19546 69304 40 19 6 22818 53117 3 12 3 32689 19764 10 2 1 5752 86680 37 14 2 22197 84105 17 17 0 20055 77945 28 19 0 25272 89113 19 14 0 82206 91005 29 11 3 32073 40248 8 4 1 5444 64187 10 16 0 20154 50857 15 20 0 36944 56613 15 12 1 8019 62792 28 15 0 30884 72535 17 16 0 19540
Names of X columns:
time blog peers shared size
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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