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Data X:
30 210907 79 112285 1418 144 28 120982 58 84786 869 103 38 176508 60 83123 1530 98 30 179321 108 101193 2172 135 22 123185 49 38361 901 61 26 52746 0 68504 463 39 25 385534 121 119182 3201 150 18 33170 1 22807 371 5 11 101645 20 17140 1192 28 26 149061 43 116174 1583 84 25 165446 69 57635 1439 80 38 237213 78 66198 1764 130 44 173326 86 71701 1495 82 30 133131 44 57793 1373 60 40 258873 104 80444 2187 131 34 180083 63 53855 1491 84 47 324799 158 97668 4041 140 30 230964 102 133824 1706 151 31 236785 77 101481 2152 91 23 135473 82 99645 1036 138 36 202925 115 114789 1882 150 36 215147 101 99052 1929 124 30 344297 80 67654 2242 119 25 153935 50 65553 1220 73 39 132943 83 97500 1289 110 34 174724 123 69112 2515 123 31 174415 73 82753 2147 90 31 225548 81 85323 2352 116 33 223632 105 72654 1638 113 25 124817 47 30727 1222 56 33 221698 105 77873 1812 115 35 210767 94 117478 1677 119 42 170266 44 74007 1579 129 43 260561 114 90183 1731 127 30 84853 38 61542 807 27 33 294424 107 101494 2452 175 13 101011 30 27570 829 35 32 215641 71 55813 1940 64 36 325107 84 79215 2662 96 0 7176 0 1423 186 0 28 167542 59 55461 1499 84 14 106408 33 31081 865 41 17 96560 42 22996 1793 47 32 265769 96 83122 2527 126 30 269651 106 70106 2747 105 35 149112 56 60578 1324 80 20 175824 57 39992 2702 70 28 152871 59 79892 1383 73 28 111665 39 49810 1179 57 39 116408 34 71570 2099 40 34 362301 76 100708 4308 68 26 78800 20 33032 918 21 39 183167 91 82875 1831 127 39 277965 115 139077 3373 154 33 150629 85 71595 1713 116 28 168809 76 72260 1438 102 4 24188 8 5950 496 7 39 329267 79 115762 2253 148 18 65029 21 32551 744 21 14 101097 30 31701 1161 35 29 218946 76 80670 2352 112 44 244052 101 143558 2144 137 21 341570 94 117105 4691 135 16 103597 27 23789 1112 26 28 233328 92 120733 2694 230 35 256462 123 105195 1973 181 28 206161 75 73107 1769 71 38 311473 128 132068 3148 147 23 235800 105 149193 2474 190 36 177939 55 46821 2084 64 32 207176 56 87011 1954 105 29 196553 41 95260 1226 107 25 174184 72 55183 1389 94 27 143246 67 106671 1496 116 36 187559 75 73511 2269 106 28 187681 114 92945 1833 143 23 119016 118 78664 1268 81 40 182192 77 70054 1943 89 23 73566 22 22618 893 26 40 194979 66 74011 1762 84 28 167488 69 83737 1403 113 34 143756 105 69094 1425 120 33 275541 116 93133 1857 110 28 243199 88 95536 1840 134 34 182999 73 225920 1502 54 30 135649 99 62133 1441 96 33 152299 62 61370 1420 78 22 120221 53 43836 1416 51 38 346485 118 106117 2970 121 26 145790 30 38692 1317 38 35 193339 100 84651 1644 145 8 80953 49 56622 870 59 24 122774 24 15986 1654 27 29 130585 67 95364 1054 91 20 112611 46 26706 937 48 29 286468 57 89691 3004 68 45 241066 75 67267 2008 58 37 148446 135 126846 2547 150 33 204713 68 41140 1885 74 33 182079 124 102860 1626 181 25 140344 33 51715 1468 65 32 220516 98 55801 2445 97 29 243060 58 111813 1964 121 28 162765 68 120293 1381 99 28 182613 81 138599 1369 152 31 232138 131 161647 1659 188 52 265318 110 115929 2888 138 21 85574 37 24266 1290 40 24 310839 130 162901 2845 254 41 225060 93 109825 1982 87 33 232317 118 129838 1904 178 32 144966 39 37510 1391 51 19 43287 13 43750 602 49 20 155754 74 40652 1743 73 31 164709 81 87771 1559 176 31 201940 109 85872 2014 94 32 235454 151 89275 2143 120 18 220801 51 44418 2146 66 23 99466 28 192565 874 56 17 92661 40 35232 1590 39 20 133328 56 40909 1590 66 12 61361 27 13294 1210 27 17 125930 37 32387 2072 65 30 100750 83 140867 1281 58 31 224549 54 120662 1401 98 10 82316 27 21233 834 25 13 102010 28 44332 1105 26 22 101523 59 61056 1272 77 42 243511 133 101338 1944 130 1 22938 12 1168 391 11 9 41566 0 13497 761 2 32 152474 106 65567 1605 101 11 61857 23 25162 530 31 25 99923 44 32334 1988 36 36 132487 71 40735 1386 120 31 317394 116 91413 2395 195 0 21054 4 855 387 4 24 209641 62 97068 1742 89 13 22648 12 44339 620 24 8 31414 18 14116 449 39 13 46698 14 10288 800 14 19 131698 60 65622 1684 78 18 91735 7 16563 1050 15 33 244749 98 76643 2699 106 40 184510 64 110681 1606 83 22 79863 29 29011 1502 24 38 128423 32 92696 1204 37 24 97839 25 94785 1138 77 8 38214 16 8773 568 16 35 151101 48 83209 1459 56 43 272458 100 93815 2158 132 43 172494 46 86687 1111 144 14 108043 45 34553 1421 40 41 328107 129 105547 2833 153 38 250579 130 103487 1955 143 45 351067 136 213688 2922 220 31 158015 59 71220 1002 79 13 98866 25 23517 1060 50 28 85439 32 56926 956 39 31 229242 63 91721 2186 95 40 351619 95 115168 3604 169 30 84207 14 111194 1035 12 16 120445 36 51009 1417 63 37 324598 113 135777 3261 134 30 131069 47 51513 1587 69 35 204271 92 74163 1424 119 32 165543 70 51633 1701 119 27 141722 19 75345 1249 75 20 116048 50 33416 946 63 18 250047 41 83305 1926 55 31 299775 91 98952 3352 103 31 195838 111 102372 1641 197 21 173260 41 37238 2035 16 39 254488 120 103772 2312 140 41 104389 135 123969 1369 89 13 136084 27 27142 1577 40 32 199476 87 135400 2201 125 18 92499 25 21399 961 21 39 224330 131 130115 1900 167 14 135781 45 24874 1254 32 7 74408 29 34988 1335 36 17 81240 58 45549 1597 13 0 14688 4 6023 207 5 30 181633 47 64466 1645 96 37 271856 109 54990 2429 151 0 7199 7 1644 151 6 5 46660 12 6179 474 13 1 17547 0 3926 141 3 16 133368 37 32755 1639 57 32 95227 37 34777 872 23 24 152601 46 73224 1318 61 17 98146 15 27114 1018 21 11 79619 42 20760 1383 43 24 59194 7 37636 1314 20 22 139942 54 65461 1335 82 12 118612 54 30080 1403 90 19 72880 14 24094 910 25 13 65475 16 69008 616 60 17 99643 33 54968 1407 61 15 71965 32 46090 771 85 16 77272 21 27507 766 43 24 49289 15 10672 473 25 15 135131 38 34029 1376 41 17 108446 22 46300 1232 26 18 89746 28 24760 1521 38 20 44296 10 18779 572 12 16 77648 31 21280 1059 29 16 181528 32 40662 1544 49 18 134019 32 28987 1230 46 22 124064 43 22827 1206 41 8 92630 27 18513 1205 31 17 121848 37 30594 1255 41 18 52915 20 24006 613 26 16 81872 32 27913 721 23 23 58981 0 42744 1109 14 22 53515 5 12934 740 16 13 60812 26 22574 1126 25 13 56375 10 41385 728 21 16 65490 27 18653 689 32 16 80949 11 18472 592 9 20 76302 29 30976 995 35 22 104011 25 63339 1613 42 17 98104 55 25568 2048 68 18 67989 23 33747 705 32 17 30989 5 4154 301 6 12 135458 43 19474 1803 68 7 73504 23 35130 799 33 17 63123 34 39067 861 84 14 61254 36 13310 1186 46 23 74914 35 65892 1451 30 17 31774 0 4143 628 0 14 81437 37 28579 1161 36 15 87186 28 51776 1463 47 17 50090 16 21152 742 20 21 65745 26 38084 979 50 18 56653 38 27717 675 30 18 158399 23 32928 1241 30 17 46455 22 11342 676 34 17 73624 30 19499 1049 33 16 38395 16 16380 620 34 15 91899 18 36874 1081 37 21 139526 28 48259 1688 83 16 52164 32 16734 736 32 14 51567 21 28207 617 30 15 70551 23 30143 812 43 17 84856 29 41369 1051 41 15 102538 50 45833 1656 51 15 86678 12 29156 705 19 10 85709 21 35944 945 37 6 34662 18 36278 554 33 22 150580 27 45588 1597 41 21 99611 41 45097 982 54 1 19349 13 3895 222 14 18 99373 12 28394 1212 25 17 86230 21 18632 1143 25 4 30837 8 2325 435 8 10 31706 26 25139 532 26 16 89806 27 27975 882 20 16 62088 13 14483 608 11 9 40151 16 13127 459 14 16 27634 2 5839 578 3 17 76990 42 24069 826 40 7 37460 5 3738 509 5 15 54157 37 18625 717 38 14 49862 17 36341 637 32 14 84337 38 24548 857 41 18 64175 37 21792 830 46 12 59382 29 26263 652 47 16 119308 32 23686 707 37 21 76702 35 49303 954 51 19 103425 17 25659 1461 49 16 70344 20 28904 672 21 1 43410 7 2781 778 1 16 104838 46 29236 1141 44 10 62215 24 19546 680 26 19 69304 40 22818 1090 21 12 53117 3 32689 616 4 2 19764 10 5752 285 10 14 86680 37 22197 1145 43 17 84105 17 20055 733 34 19 77945 28 25272 888 32 14 89113 19 82206 849 20 11 91005 29 32073 1182 34 4 40248 8 5444 528 6 16 64187 10 20154 642 12 20 50857 15 36944 947 24 12 56613 15 8019 819 16 15 62792 28 30884 757 72 16 72535 17 19540 894 27
Names of X columns:
compendiums_reviewed time_in_rfc blogged_computations totsize pageviews tothyperlinks
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
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2
3
4
5
6
7
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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 Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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