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Data X:
162556 1081 213118 29790 309 81767 87550 458 153198 84738 588 -26007 54660 302 126942 42634 156 157214 40949 481 129352 45187 353 234817 37704 452 60448 16275 109 47818 25830 115 245546 12679 110 48020 18014 239 -1710 43556 247 32648 24811 505 95350 6575 159 151352 7123 109 288170 21950 519 114337 37597 248 37884 17821 373 122844 12988 119 82340 22330 84 79801 13326 102 165548 16189 295 116384 7146 105 134028 15824 64 63838 27664 282 74996 11920 182 31080 8568 37 32168 14416 361 49857 3369 28 87161 11819 85 106113 6984 45 80570 4519 49 102129 2220 22 301670 18562 155 102313 10327 91 88577 5336 81 112477 2365 79 191778 4069 145 79804 8636 855 128294 13718 61 96448 4525 226 93811 6869 105 117520 4628 62 69159 3689 25 101792 4891 217 210568 7489 322 136996 4901 84 121920 2284 33 76403 3160 108 108094 4150 150 134759 7285 115 188873 1134 162 146216 4658 158 156608 2384 97 61348 3748 9 50350 5371 66 87720 1285 107 99489 9327 101 87419 5565 47 94355 1528 38 60326 3122 34 94670 7561 87 82425 2675 79 59017 13253 947 90829 880 74 80791 2053 53 100423 1424 94 131116 4036 63 100269 3045 58 27330 5119 49 39039 1431 34 106885 554 11 79285 1975 35 118881 1765 20 77623 1012 47 114768 810 43 74015 1280 117 69465 666 171 117869 1380 26 60982 4677 75 90131 876 59 138971 814 18 39625 514 15 102725 5692 72 64239 3642 86 90262 540 14 103960 2099 64 106611 567 11 103345 2001 52 95551 2949 41 82903 2253 99 63593 6533 75 126910 1889 45 37527 3055 43 60247 272 8 112995 1414 198 70184 2564 22 130140 1383 11 73221 1261 33 76114 975 23 90534 3366 80 108479 576 18 113761 1686 40 68696 746 23 71561 3192 60 59831 2045 20 97890 5702 61 101481 1932 36 72954 936 30 67939 3437 47 48022 5131 71 86111 2397 14 74020 1389 9 57530 1503 39 56364 402 26 84990 2239 21 88590 2234 16 77200 837 69 61262 10579 92 110309 875 14 67000 1585 107 93099 1659 29 107577 2647 37 62920 3294 23 75832 0 0 60720 94 7 60793 422 28 57935 0 0 60720 34 8 60630 1558 63 55637 0 0 60720 43 3 60887 645 5 60720 316 9 60505 115 13 60945 5 2 60720 897 5 60720 0 0 60720 389 14 58990 0 0 60720 1002 15 56750 36 3 60894 460 15 63346 309 11 56535 0 0 60720 9 6 60835 271 2 60720 14 1 61016 520 10 58650 1766 73 60438 0 0 60720 458 11 58625 20 3 60938 0 0 60720 0 0 60720 98 2 61490 405 7 60845 0 0 60720 0 0 60720 0 0 60720 0 0 60720 483 27 60830 454 51 63261 47 3 60720 0 0 60720 757 19 45689 4655 393 60720 0 0 60720 0 0 60720 36 4 61564 0 0 60720 203 9 61938 0 0 60720 126 10 60951 400 152 60720 71 1 60745 0 0 60720 0 0 60720 972 34 71642 531 10 71641 2461 57 55792 378 52 71873 23 5 62555 638 14 60370 2300 29 64873 149 5 62041 226 5 65745 0 0 60720 275 4 59500 0 0 60720 141 6 61630 0 0 60720 28 2 60890 0 0 60720 4980 91 113521 0 0 60720 0 0 60720 472 20 80045 0 0 60720 0 0 60720 0 0 60720 203 27 50804 496 17 87390 10 2 61656 63 4 65688 0 0 60720 1136 32 48522 265 31 60720 0 0 60720 0 0 60720 267 32 57640 474 20 61977 534 7 62620 0 0 60720 15 8 60831 397 28 60646 0 0 60720 1866 29 56225 288 4 60510 0 0 60720 3 2 60698 468 21 60720 20 2 60805 278 26 61404 61 14 60720 0 0 60720 192 4 65276 0 0 60720 317 9 63915 738 10 60720 0 0 60720 368 17 61686 0 0 60720 2 1 60743 0 0 60720 53 6 60349 0 0 60720 0 0 60720 0 0 60720 94 3 61360 0 0 60720 24 8 59818 2332 4 72680 0 0 60720 0 0 60720 131 11 61808 0 0 60720 0 0 60720 206 9 53110 0 0 60720 167 2 64245 622 73 73007 2328 94 82732 0 0 60720 365 8 54820 364 35 47705 0 0 60720 0 0 60720 0 0 60720 0 0 60720 226 12 72835 307 15 58856 0 0 60720 0 0 60720 0 0 60720 188 11 77655 0 0 60720 138 6 69817 0 0 60720 0 0 60720 0 0 60720 125 12 60798 0 0 60720 282 30 62452 335 33 64175 0 0 60720 1324 117 67440 176 28 68136 0 0 60720 0 0 60720 249 72 56726 0 0 60720 333 13 70811 0 0 60720 601 6 60720 30 4 62045 0 0 60720 249 62 54323 0 0 60720 165 24 62841 453 21 81125 0 0 60720 53 14 59506 382 21 59365 0 0 60720 0 0 60720 0 0 60720 0 0 60720 30 4 60798 290 2 58790 0 0 60720 0 0 60720 366 53 61808 2 9 60735 0 0 60720 209 13 64016 384 22 54683 0 0 60720 0 0 60720 365 83 87192 0 0 60720 49 8 64107 3 4 60761 133 14 65990 32 1 59988 368 17 61167 1 6 60719 0 0 60720 0 0 60720 0 0 60720 0 0 60720 0 0 60720 0 0 60720 22 2 60722 0 0 60720 0 0 60720 0 0 60720 0 0 60720 0 0 60720 0 0 60720 0 0 60720 96 5 60379 1 2 60727 314 5 60720 844 78 60925 0 0 60720 26 1 60896 125 13 59734 304 15 62969 0 0 60720 0 0 60720 0 0 60720 621 48 60720 0 0 60720 119 6 59118 0 0 60720 0 0 60720 1595 17 60720 312 14 58598 60 10 61124 587 12 59595 135 2 62065 0 0 60720 0 0 60720 514 52 78780 0 0 60720 0 0 60720 0 0 60720 1 4 60722 0 0 60720 0 0 60720 1763 24 61600 180 11 59635 0 0 60720 0 0 60720 0 0 60720 0 0 60720 218 21 60720 0 0 60720 448 40 59781 227 9 76644 174 1 64820 0 0 60720 0 0 60720 121 24 56178 607 11 60436 2212 14 60720 0 0 60720 0 0 60720 530 60 73433 571 80 41477 0 0 60720 78 16 62700 2489 40 67804 131 6 59661 923 8 58620 72 3 60398 572 16 58580 397 10 62710 450 8 59325 622 7 60950 694 8 68060 3425 12 83620 562 13 58456 4917 42 52811 1442 118 121173 529 9 63870 2126 138 21001 1061 5 70415 776 9 64230 611 8 59190 1526 25 69351 592 7 64270 1182 13 70694 621 16 68005 989 11 58930 438 11 58320 726 3 69980 1303 61 69863 7419 29 63255 1164 17 57320 3310 33 75230 1920 15 79420 965 3 73490 3256 66 35250 1135 17 62285 1270 26 69206 661 3 65920 1013 2 69770 2844 67 72683 11528 70 -14545 6526 26 55830 2264 24 55174 5109 97 67038 3999 30 51252 35624 223 157278 9252 48 79510 15236 90 77440 18073 180 27284
Names of X columns:
Costs Trades Dividends
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
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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Summary of computational transaction
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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