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Data X:
1418 210907 56 79 30 869 120982 56 58 28 1530 176508 54 60 38 2172 179321 89 108 30 901 123185 40 49 22 463 52746 25 0 26 3201 385534 92 121 25 371 33170 18 1 18 1192 101645 63 20 11 1583 149061 44 43 26 1439 165446 33 69 25 1764 237213 84 78 38 1495 173326 88 86 44 1373 133131 55 44 30 2187 258873 60 104 40 1491 180083 66 63 34 4041 324799 154 158 47 1706 230964 53 102 30 2152 236785 119 77 31 1036 135473 41 82 23 1882 202925 61 115 36 1929 215147 58 101 36 2242 344297 75 80 30 1220 153935 33 50 25 1289 132943 40 83 39 2515 174724 92 123 34 2147 174415 100 73 31 2352 225548 112 81 31 1638 223632 73 105 33 1222 124817 40 47 25 1812 221698 45 105 33 1677 210767 60 94 35 1579 170266 62 44 42 1731 260561 75 114 43 807 84853 31 38 30 2452 294424 77 107 33 829 101011 34 30 13 1940 215641 46 71 32 2662 325107 99 84 36 186 7176 17 0 0 1499 167542 66 59 28 865 106408 30 33 14 1793 96560 76 42 17 2527 265769 146 96 32 2747 269651 67 106 30 1324 149112 56 56 35 2702 175824 107 57 20 1383 152871 58 59 28 1179 111665 34 39 28 2099 116408 61 34 39 4308 362301 119 76 34 918 78800 42 20 26 1831 183167 66 91 39 3373 277965 89 115 39 1713 150629 44 85 33 1438 168809 66 76 28 496 24188 24 8 4 2253 329267 259 79 39 744 65029 17 21 18 1161 101097 64 30 14 2352 218946 41 76 29 2144 244052 68 101 44 4691 341570 168 94 21 1112 103597 43 27 16 2694 233328 132 92 28 1973 256462 105 123 35 1769 206161 71 75 28 3148 311473 112 128 38 2474 235800 94 105 23 2084 177939 82 55 36 1954 207176 70 56 32 1226 196553 57 41 29 1389 174184 53 72 25 1496 143246 103 67 27 2269 187559 121 75 36 1833 187681 62 114 28 1268 119016 52 118 23 1943 182192 52 77 40 893 73566 32 22 23 1762 194979 62 66 40 1403 167488 45 69 28 1425 143756 46 105 34 1857 275541 63 116 33 1840 243199 75 88 28 1502 182999 88 73 34 1441 135649 46 99 30 1420 152299 53 62 33 1416 120221 37 53 22 2970 346485 90 118 38 1317 145790 63 30 26 1644 193339 78 100 35 870 80953 25 49 8 1654 122774 45 24 24 1054 130585 46 67 29 937 112611 41 46 20 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17 1383 79619 43 42 11 1314 59194 31 7 24 1335 139942 42 54 22 1403 118612 46 54 12 910 72880 33 14 19 616 65475 18 16 13 1407 99643 55 33 17 771 71965 35 32 15 766 77272 59 21 16 473 49289 19 15 24 1376 135131 66 38 15 1232 108446 60 22 17 1521 89746 36 28 18 572 44296 25 10 20 1059 77648 47 31 16 1544 181528 54 32 16 1230 134019 53 32 18 1206 124064 40 43 22 1205 92630 40 27 8 1255 121848 39 37 17 613 52915 14 20 18 721 81872 45 32 16 1109 58981 36 0 23 740 53515 28 5 22 1126 60812 44 26 13 728 56375 30 10 13 689 65490 22 27 16 592 80949 17 11 16 995 76302 31 29 20 1613 104011 55 25 22 2048 98104 54 55 17 705 67989 21 23 18 301 30989 14 5 17 1803 135458 81 43 12 799 73504 35 23 7 861 63123 43 34 17 1186 61254 46 36 14 1451 74914 30 35 23 628 31774 23 0 17 1161 81437 38 37 14 1463 87186 54 28 15 742 50090 20 16 17 979 65745 53 26 21 675 56653 45 38 18 1241 158399 39 23 18 676 46455 20 22 17 1049 73624 24 30 17 620 38395 31 16 16 1081 91899 35 18 15 1688 139526 151 28 21 736 52164 52 32 16 617 51567 30 21 14 812 70551 31 23 15 1051 84856 29 29 17 1656 102538 57 50 15 705 86678 40 12 15 945 85709 44 21 10 554 34662 25 18 6 1597 150580 77 27 22 982 99611 35 41 21 222 19349 11 13 1 1212 99373 63 12 18 1143 86230 44 21 17 435 30837 19 8 4 532 31706 13 26 10 882 89806 42 27 16 608 62088 38 13 16 459 40151 29 16 9 578 27634 20 2 16 826 76990 27 42 17 509 37460 20 5 7 717 54157 19 37 15 637 49862 37 17 14 857 84337 26 38 14 830 64175 42 37 18 652 59382 49 29 12 707 119308 30 32 16 954 76702 49 35 21 1461 103425 67 17 19 672 70344 28 20 16 778 43410 19 7 1 1141 104838 49 46 16 680 62215 27 24 10 1090 69304 30 40 19 616 53117 22 3 12 285 19764 12 10 2 1145 86680 31 37 14 733 84105 20 17 17 888 77945 20 28 19 849 89113 39 19 14 1182 91005 29 29 11 528 40248 16 8 4 642 64187 27 10 16 947 50857 21 15 20 819 56613 19 15 12 757 62792 35 28 15 894 72535 14 17 16
Names of X columns:
pageviews time_in_rfc logins blogged_computations compendiums_reviewed
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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7
8
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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
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R Server
Big Analytics Cloud Computing Center
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