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