Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
162556 1081 807 213118 29790 309 444 81767 87550 458 412 153198 84738 588 428 -26007 54660 302 315 126942 42634 156 168 157214 40949 481 263 129352 45187 353 267 234817 37704 452 228 60448 16275 109 129 47818 25830 115 104 245546 12679 110 122 48020 18014 239 393 -1710 43556 247 190 32648 24811 505 280 95350 6575 159 63 151352 7123 109 102 288170 21950 519 265 114337 37597 248 234 37884 17821 373 277 122844 12988 119 73 82340 22330 84 67 79801 13326 102 103 165548 16189 295 290 116384 7146 105 83 134028 15824 64 56 63838 27664 282 236 74996 11920 182 73 31080 8568 37 34 32168 14416 361 139 49857 3369 28 26 87161 11819 85 70 106113 6984 45 40 80570 4519 49 42 102129 2220 22 12 301670 18562 155 211 102313 10327 91 74 88577 5336 81 80 112477 2365 79 83 191778 4069 145 131 79804 8636 855 203 128294 13718 61 56 96448 4525 226 89 93811 6869 105 88 117520 4628 62 39 69159 3689 25 25 101792 4891 217 49 210568 7489 322 149 136996 4901 84 58 121920 2284 33 41 76403 3160 108 90 108094 4150 150 136 134759 7285 115 97 188873 1134 162 63 146216 4658 158 114 156608 2384 97 77 61348 3748 9 6 50350 5371 66 47 87720 1285 107 51 99489 9327 101 85 87419 5565 47 43 94355 1528 38 32 60326 3122 34 25 94670 7561 87 77 82425 2675 79 54 59017 13253 947 251 90829 880 74 15 80791 2053 53 44 100423 1424 94 73 131116 4036 63 85 100269 3045 58 49 27330 5119 49 38 39039 1431 34 35 106885 554 11 9 79285 1975 35 34 118881 1765 20 20 77623 1012 47 29 114768 810 43 11 74015 1280 117 52 69465 666 171 13 117869 1380 26 29 60982 4677 75 66 90131 876 59 33 138971 814 18 15 39625 514 15 15 102725 5692 72 68 64239 3642 86 100 90262 540 14 13 103960 2099 64 45 106611 567 11 14 103345 2001 52 36 95551 2949 41 40 82903 2253 99 68 63593 6533 75 29 126910 1889 45 43 37527 3055 43 30 60247 272 8 9 112995 1414 198 22 70184 2564 22 19 130140 1383 11 9 73221 1261 33 31 76114 975 23 19 90534 3366 80 55 108479 576 18 8 113761 1686 40 28 68696 746 23 29 71561 3192 60 48 59831 2045 20 16 97890 5702 61 47 101481 1932 36 20 72954 936 30 22 67939 3437 47 33 48022 5131 71 44 86111 2397 14 13 74020 1389 9 6 57530 1503 39 35 56364 402 26 8 84990 2239 21 17 88590 2234 16 11 77200 837 69 21 61262 10579 92 92 110309 875 14 12 67000 1585 107 112 93099 1659 29 25 107577 2647 37 17 62920 3294 23 23 75832 0 0 0 60720 94 7 10 60793 422 28 23 57935 0 0 0 60720 34 8 7 60630 1558 63 25 55637 0 0 0 60720 43 3 20 60887 645 5 4 60720 316 9 4 60505 115 13 10 60945 5 2 1 60720 897 5 4 60720 0 0 0 60720 389 14 8 58990 0 0 0 60720 1002 15 11 56750 36 3 4 60894 460 15 15 63346 309 11 9 56535 0 0 0 60720 9 6 7 60835 271 2 2 60720 14 1 0 61016 520 10 7 58650 1766 73 46 60438 0 0 5 60720 458 11 7 58625 20 3 2 60938 0 0 0 60720 0 0 0 60720 98 2 2 61490 405 7 5 60845 0 0 0 60720 0 0 0 60720 0 0 0 60720 0 0 0 60720 483 27 7 60830 454 51 24 63261 47 3 1 60720 0 0 0 60720 757 19 18 45689 4655 393 55 60720 0 0 0 60720 0 0 0 60720 36 4 3 61564 0 0 0 60720 203 9 9 61938 0 0 0 60720 126 10 8 60951 400 152 113 60720 71 1 0 60745 0 0 0 60720 0 0 0 60720 972 34 19 71642 531 10 11 71641 2461 57 25 55792 378 52 16 71873 23 5 5 62555 638 14 11 60370 2300 29 23 64873 149 5 6 62041 226 5 5 65745 0 0 0 60720 275 4 7 59500 0 0 0 60720 141 6 7 61630 0 0 0 60720 28 2 3 60890 0 0 0 60720 4980 91 89 113521 0 0 0 60720 0 0 0 60720 472 20 19 80045 0 0 0 60720 0 0 0 60720 0 0 0 60720 203 27 12 50804 496 17 12 87390 10 2 5 61656 63 4 2 65688 0 0 0 60720 1136 32 26 48522 265 31 3 60720 0 0 0 60720 0 0 0 60720 267 32 11 57640 474 20 10 61977 534 7 5 62620 0 0 2 60720 15 8 6 60831 397 28 7 60646 0 0 2 60720 1866 29 28 56225 288 4 3 60510 0 0 0 60720 3 2 1 60698 468 21 20 60720 20 2 1 60805 278 26 22 61404 61 14 9 60720 0 0 0 60720 192 4 2 65276 0 0 0 60720 317 9 7 63915 738 10 9 60720 0 0 0 60720 368 17 13 61686 0 0 0 60720 2 1 0 60743 0 0 0 60720 53 6 6 60349 0 0 0 60720 0 0 0 60720 0 0 0 60720 94 3 3 61360 0 0 0 60720 24 8 7 59818 2332 4 2 72680 0 0 0 60720 0 0 0 60720 131 11 15 61808 0 0 0 60720 0 0 0 60720 206 9 9 53110 0 0 0 60720 167 2 1 64245 622 73 38 73007 2328 94 57 82732 0 0 0 60720 365 8 7 54820 364 35 26 47705 0 0 0 60720 0 0 0 60720 0 0 0 60720 0 0 0 60720 226 12 13 72835 307 15 10 58856 0 0 0 60720 0 0 0 60720 0 0 0 60720 188 11 9 77655 0 0 0 60720 138 6 26 69817 0 0 0 60720 0 0 0 60720 0 0 0 60720 125 12 19 60798 0 0 0 60720 282 30 12 62452 335 33 23 64175 0 0 0 60720 1324 117 29 67440 176 28 8 68136 0 0 0 60720 0 0 0 60720 249 72 26 56726 0 0 0 60720 333 13 9 70811 0 0 0 60720 601 6 5 60720 30 4 3 62045 0 0 0 60720 249 62 13 54323 0 0 0 60720 165 24 12 62841 453 21 19 81125 0 0 0 60720 53 14 10 59506 382 21 9 59365 0 0 0 60720 0 0 0 60720 0 0 0 60720 0 0 9 60720 30 4 4 60798 290 2 1 58790 0 0 1 60720 0 0 0 60720 366 53 14 61808 2 9 12 60735 0 0 0 60720 209 13 19 64016 384 22 17 54683 0 0 0 60720 0 0 0 60720 365 83 32 87192 0 0 0 60720 49 8 14 64107 3 4 8 60761 133 14 4 65990 32 1 0 59988 368 17 20 61167 1 6 5 60719 0 0 0 60720 0 0 0 60720 0 0 0 60720 0 0 0 60720 0 0 0 60720 0 0 0 60720 22 2 1 60722 0 0 0 60720 0 0 0 60720 0 0 0 60720 0 0 0 60720 0 0 0 60720 0 0 0 60720 0 0 0 60720 96 5 4 60379 1 2 1 60727 314 5 4 60720 844 78 20 60925 0 0 0 60720 26 1 1 60896 125 13 10 59734 304 15 12 62969 0 0 0 60720 0 0 0 60720 0 0 0 60720 621 48 13 60720 0 0 0 60720 119 6 3 59118 0 0 0 60720 0 0 0 60720 1595 17 10 60720 312 14 3 58598 60 10 7 61124 587 12 10 59595 135 2 1 62065 0 0 0 60720 0 0 0 60720 514 52 15 78780 0 0 0 60720 0 0 0 60720 0 0 0 60720 1 4 4 60722 0 0 0 60720 0 0 0 60720 1763 24 28 61600 180 11 9 59635 0 0 0 60720 0 0 0 60720 0 0 0 60720 0 0 0 60720 218 21 7 60720 0 0 0 60720 448 40 7 59781 227 9 7 76644 174 1 3 64820 0 0 0 60720 0 0 0 60720 121 24 11 56178 607 11 7 60436 2212 14 10 60720 0 0 0 60720 0 0 0 60720 530 60 18 73433 571 80 14 41477 0 0 0 60720 78 16 12 62700 2489 40 29 67804 131 6 3 59661 923 8 6 58620 72 3 3 60398 572 16 8 58580 397 10 10 62710 450 8 6 59325 622 7 8 60950 694 8 6 68060 3425 12 9 83620 562 13 8 58456 4917 42 26 52811 1442 118 239 121173 529 9 7 63870 2126 138 41 21001 1061 5 3 70415 776 9 8 64230 611 8 6 59190 1526 25 21 69351 592 7 7 64270 1182 13 11 70694 621 16 11 68005 989 11 12 58930 438 11 9 58320 726 3 3 69980 1303 61 57 69863 7419 29 21 63255 1164 17 15 57320 3310 33 32 75230 1920 15 11 79420 965 3 2 73490 3256 66 23 35250 1135 17 20 62285 1270 26 24 69206 661 3 1 65920 1013 2 1 69770 2844 67 74 72683 11528 70 68 -14545 6526 26 20 55830 2264 24 20 55174 5109 97 82 67038 3999 30 21 51252 35624 223 244 157278 9252 48 32 79510 15236 90 86 77440 18073 180 69 27284
Names of X columns:
Costs Trades Orders 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') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation