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