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Data X:
Costs Orders Dividends Wealth 162556 807 213118 6282154 29790 444 81767 4321023 87550 412 153198 4111912 84738 428 -26007 223193 54660 315 126942 1491348 42634 168 157214 1629616 40949 263 129352 1398893 45187 267 234817 1926517 37704 228 60448 983660 16275 129 47818 1443586 25830 104 245546 1073089 12679 122 48020 984885 18014 393 -1710 1405225 43556 190 32648 227132 24811 280 95350 929118 6575 63 151352 1071292 7123 102 288170 638830 21950 265 114337 856956 37597 234 37884 992426 17821 277 122844 444477 12988 73 82340 857217 22330 67 79801 711969 13326 103 165548 702380 16189 290 116384 358589 7146 83 134028 297978 15824 56 63838 585715 27664 236 74996 657954 11920 73 31080 209458 8568 34 32168 786690 14416 139 49857 439798 3369 26 87161 688779 11819 70 106113 574339 6984 40 80570 741409 4519 42 102129 597793 2220 12 301670 644190 18562 211 102313 377934 10327 74 88577 640273 5336 80 112477 697458 2365 83 191778 550608 4069 131 79804 207393 8636 203 128294 301607 13718 56 96448 345783 4525 89 93811 501749 6869 88 117520 379983 4628 39 69159 387475 3689 25 101792 377305 4891 49 210568 370837 7489 149 136996 430866 4901 58 121920 469107 2284 41 76403 194493 3160 90 108094 530670 4150 136 134759 518365 7285 97 188873 491303 1134 63 146216 527021 4658 114 156608 233773 2384 77 61348 405972 3748 6 50350 652925 5371 47 87720 446211 1285 51 99489 341340 9327 85 87419 387699 5565 43 94355 493408 1528 32 60326 146494 3122 25 94670 414462 7561 77 82425 364304 2675 54 59017 355178 13253 251 90829 357760 880 15 80791 261216 2053 44 100423 397144 1424 73 131116 374943 4036 85 100269 424898 3045 49 27330 202055 5119 38 39039 378525 1431 35 106885 310768 554 9 79285 325738 1975 34 118881 394510 1765 20 77623 247060 1012 29 114768 368078 810 11 74015 236761 1280 52 69465 312378 666 13 117869 339836 1380 29 60982 347385 4677 66 90131 426280 876 33 138971 352850 814 15 39625 301881 514 15 102725 377516 5692 68 64239 357312 3642 100 90262 458343 540 13 103960 354228 2099 45 106611 308636 567 14 103345 386212 2001 36 95551 393343 2949 40 82903 378509 2253 68 63593 452469 6533 29 126910 364839 1889 43 37527 358649 3055 30 60247 376641 272 9 112995 429112 1414 22 70184 330546 2564 19 130140 403560 1383 9 73221 317892 1261 31 76114 307528 975 19 90534 235133 3366 55 108479 299243 576 8 113761 314073 1686 28 68696 368186 746 29 71561 269661 3192 48 59831 125390 2045 16 97890 510834 5702 47 101481 321896 1932 20 72954 249898 936 22 67939 408881 3437 33 48022 158492 5131 44 86111 292154 2397 13 74020 289513 1389 6 57530 378049 1503 35 56364 343466 402 8 84990 332743 2239 17 88590 442882 2234 11 77200 214215 837 21 61262 315688 10579 92 110309 375195 875 12 67000 334280 1585 112 93099 355864 1659 25 107577 480382 2647 17 62920 353058 3294 23 75832 217193 0 0 60720 315380 94 10 60793 314533 422 23 57935 318056 0 0 60720 315380 34 7 60630 314353 1558 25 55637 369448 0 0 60720 315380 43 20 60887 312846 645 4 60720 312075 316 4 60505 315009 115 10 60945 318903 5 1 60720 314887 897 4 60720 314913 0 0 60720 315380 389 8 58990 325506 0 0 60720 315380 1002 11 56750 298568 36 4 60894 315834 460 15 63346 329784 309 9 56535 312878 0 0 60720 315380 9 7 60835 314987 271 2 60720 325249 14 0 61016 315877 520 7 58650 291650 1766 46 60438 305959 0 5 60720 315380 458 7 58625 297765 20 2 60938 315245 0 0 60720 315380 0 0 60720 315380 98 2 61490 315236 405 5 60845 336425 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 483 7 60830 306268 454 24 63261 302187 47 1 60720 314882 0 0 60720 315380 757 18 45689 382712 4655 55 60720 341570 0 0 60720 315380 0 0 60720 315380 36 3 61564 312412 0 0 60720 315380 203 9 61938 309596 0 0 60720 315380 126 8 60951 315547 400 113 60720 313267 71 0 60745 316176 0 0 60720 315380 0 0 60720 315380 972 19 71642 359335 531 11 71641 330068 2461 25 55792 314289 378 16 71873 297413 23 5 62555 314806 638 11 60370 333210 2300 23 64873 352108 149 6 62041 313332 226 5 65745 291787 0 0 60720 315380 275 7 59500 318745 0 0 60720 315380 141 7 61630 315366 0 0 60720 315380 28 3 60890 315688 0 0 60720 315380 4980 89 113521 409642 0 0 60720 315380 0 0 60720 315380 472 19 80045 269587 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 203 12 50804 300962 496 12 87390 325479 10 5 61656 316155 63 2 65688 318574 0 0 60720 315380 1136 26 48522 343613 265 3 60720 306948 0 0 60720 315380 0 0 60720 315380 267 11 57640 330059 474 10 61977 288985 534 5 62620 304485 0 2 60720 315380 15 6 60831 315688 397 7 60646 317736 0 2 60720 315380 1866 28 56225 322331 288 3 60510 296656 0 0 60720 315380 3 1 60698 315354 468 20 60720 312161 20 1 60805 315576 278 22 61404 314922 61 9 60720 314551 0 0 60720 315380 192 2 65276 312339 0 0 60720 315380 317 7 63915 298700 738 9 60720 321376 0 0 60720 315380 368 13 61686 303230 0 0 60720 315380 2 0 60743 315487 0 0 60720 315380 53 6 60349 315793 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 94 3 61360 312887 0 0 60720 315380 24 7 59818 315637 2332 2 72680 324385 0 0 60720 315380 0 0 60720 315380 131 15 61808 308989 0 0 60720 315380 0 0 60720 315380 206 9 53110 296702 0 0 60720 315380 167 1 64245 307322 622 38 73007 304376 2328 57 82732 253588 0 0 60720 315380 365 7 54820 309560 364 26 47705 298466 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 226 13 72835 343929 307 10 58856 331955 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 188 9 77655 381180 0 0 60720 315380 138 26 69817 331420 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 125 19 60798 310201 0 0 60720 315380 282 12 62452 320016 335 23 64175 320398 0 0 60720 315380 1324 29 67440 291841 176 8 68136 310670 0 0 60720 315380 0 0 60720 315380 249 26 56726 313491 0 0 60720 315380 333 9 70811 331323 0 0 60720 315380 601 5 60720 319210 30 3 62045 318098 0 0 60720 315380 249 13 54323 292754 0 0 60720 315380 165 12 62841 325176 453 19 81125 365959 0 0 60720 315380 53 10 59506 302409 382 9 59365 340968 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 0 9 60720 315380 30 4 60798 313164 290 1 58790 301164 0 1 60720 315380 0 0 60720 315380 366 14 61808 344425 2 12 60735 315394 0 0 60720 315380 209 19 64016 316647 384 17 54683 309836 0 0 60720 315380 0 0 60720 315380 365 32 87192 346611 0 0 60720 315380 49 14 64107 322031 3 8 60761 315656 133 4 65990 339445 32 0 59988 314964 368 20 61167 297141 1 5 60719 315372 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 22 1 60722 312502 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 96 4 60379 313729 1 1 60727 315388 314 4 60720 315371 844 20 60925 296139 0 0 60720 315380 26 1 60896 313880 125 10 59734 317698 304 12 62969 295580 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 621 13 60720 308256 0 0 60720 315380 119 3 59118 303677 0 0 60720 315380 0 0 60720 315380 1595 10 60720 319369 312 3 58598 318690 60 7 61124 314049 587 10 59595 325699 135 1 62065 314210 0 0 60720 315380 0 0 60720 315380 514 15 78780 322378 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 1 4 60722 315398 0 0 60720 315380 0 0 60720 315380 1763 28 61600 308336 180 9 59635 316386 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 0 0 60720 315380 218 7 60720 315553 0 0 60720 315380 448 7 59781 323361 227 7 76644 336639 174 3 64820 307424 0 0 60720 315380 0 0 60720 315380 121 11 56178 295370 607 7 60436 322340 2212 10 60720 319864 0 0 60720 315380 0 0 60720 315380 530 18 73433 317291 571 14 41477 280398 0 0 60720 315380 78 12 62700 317330 2489 29 67804 238125 131 3 59661 327071 923 6 58620 309038 72 3 60398 314210 572 8 58580 307930 397 10 62710 322327 450 6 59325 292136 622 8 60950 263276 694 6 68060 367655 3425 9 83620 283910 562 8 58456 283587 4917 26 52811 243650 1442 239 121173 438493 529 7 63870 296261 2126 41 21001 230621 1061 3 70415 304252 776 8 64230 333505 611 6 59190 296919 1526 21 69351 278990 592 7 64270 276898 1182 11 70694 327007 621 11 68005 317046 989 12 58930 304555 438 9 58320 298096 726 3 69980 231861 1303 57 69863 309422 7419 21 63255 286963 1164 15 57320 269753 3310 32 75230 448243 1920 11 79420 165404 965 2 73490 204325 3256 23 35250 407159 1135 20 62285 290476 1270 24 69206 275311 661 1 65920 246541 1013 1 69770 253468 2844 74 72683 240897 11528 68 -14545 -83265 6526 20 55830 -42143 2264 20 55174 272713 5109 82 67038 215362 3999 21 51252 42754 35624 244 157278 306275 9252 32 79510 253537 15236 86 77440 372631 18073 69 27284 -7170
Names of X columns:
Costs orders dividends wealth

Sample Range: (leave blank to include all observations)
From:
To:

Column Number of Endogenous Series (?)


Fixed Seasonal Effects


Type of Equation


Degree of Predetermination (lagged endogenous variables)


Degree of Seasonal Predetermination


Seasonality


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') }

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	Big Analytics Cloud Computing Center
R Engine error message	Error in array(list(Costs, Orders, Dividends, Wealth, 162556, 807, 213118, : object 'Costs' not found Execution halted