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Data X:
1 1595 17 60720 319369 0 5565 47 94355 493408 0 601 6 60720 319210 1 188 11 77655 381180 1 7146 105 134028 297978 0 1135 17 62285 290476 1 450 8 59325 292136 1 34 8 60630 314353 1 133 14 65990 339445 1 119 6 59118 303677 0 2053 53 100423 397144 0 4036 63 100269 424898 1 0 0 60720 315380 1 4655 393 60720 341570 1 131 11 61808 308989 1 1766 73 60438 305959 1 312 14 58598 318690 1 448 40 59781 323361 1 115 13 60945 318903 1 60 10 61124 314049 1 0 0 60720 315380 1 364 35 47705 298466 1 0 0 60720 315380 0 1442 118 121173 438493 0 1389 9 57530 378049 1 149 5 62041 313332 1 2212 14 60720 319864 0 7489 322 136996 430866 1 0 0 60720 315380 1 402 26 84990 332743 0 7419 29 63255 286963 1 0 0 60720 315380 1 307 15 58856 331955 0 1134 162 146216 527021 0 7561 87 82425 364304 1 5131 71 86111 292154 1 9 6 60835 314987 1 2264 24 55174 272713 1 40949 481 129352 1398893 0 4980 91 113521 409642 1 272 8 112995 429112 1 757 19 45689 382712 1 1424 94 131116 374943 1 0 0 60720 315380 0 378 52 71873 297413 1 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60720 315380 1 206 9 53110 296702 1 25830 115 245546 1073089 1 1528 38 60326 146494 1 271 2 60720 325249 1 138 6 69817 331420 1 0 0 60720 315380 1 278 26 61404 314922 1 282 30 62452 320016 1 571 80 41477 280398 1 2253 99 63593 452469 1 290 2 58790 301164 1 78 16 62700 317330 1 20 2 60805 315576 1 0 0 60720 315380 1 18073 180 27284 -7170 1 1866 29 56225 322331 0 42634 156 157214 1629616 1 249 62 54323 292754 1 422 28 57935 318056 1 2675 79 59017 355178 1 965 3 73490 204325 1 0 0 60720 315380 1 621 16 68005 317046 0 0 0 60720 315380 1 365 8 54820 309560 0 3122 34 94670 414462 0 12988 119 82340 857217 0 5336 81 112477 697458 0 3160 108 108094 530670 1 2489 40 67804 238125 1 0 0 60720 315380 0 6984 45 80570 741409 1 2001 52 95551 393343 1 15236 90 77440 372631 0 0 0 60720 315380 1 530 60 73433 317291 1 0 0 60720 315380 1 35624 223 157278 306275 0 1383 11 73221 317892 1 875 14 67000 334280 0 0 0 60720 315380 0 0 0 60720 315380 0 0 0 60720 315380 1 72 3 60398 314210 0 265 31 60720 306948 1 0 0 60720 315380 1 335 33 64175 320398 0 0 0 60720 315380 1 0 0 60720 315380 1 4525 226 93811 501749 0 3045 58 27330 202055 1 0 0 60720 315380 1 0 0 60720 315380 0 638 14 60370 333210 0 0 0 60720 315380 1 607 11 60436 322340 1 1558 63 55637 369448 0 1324 117 67440 291841 1 0 0 60720 315380 0 0 0 60720 315380 1 611 8 59190 296919 1 0 0 60720 315380 1 923 8 58620 309038 1 661 3 65920 246541 0 2397 14 74020 289513 1 366 53 61808 344425 1 0 0 60720 315380 1 135 2 62065 314210 0 1659 29 107577 480382 1 316 9 60505 315009 1 0 0 60720 315380 1 309 11 56535 312878 0 49 8 64107 322031 1 0 0 60720 315380 0 4519 49 102129 597793 1 0 0 60720 315380 1 837 69 61262 315688 1 5119 49 39039 378525 1 1280 117 69465 312378 1 2564 22 130140 403560 0 2045 20 97890 510834 0 2234 16 77200 214215 1 975 23 90534 235133 0 1136 32 48522 343613 1 453 21 81125 365959 1 0 0 60720 315380 1 61 14 60720 314551 1 368 17 61686 303230 0 0 0 60720 315380 1 4901 84 121920 469107 1 540 14 103960 354228 1 0 0 60720 315380 1 0 0 60720 315380 0 0 0 60720 315380 1 2 9 60735 315394 1 36 4 61564 312412 1 776 9 64230 333505 1 84738 588 -26007 223193 1 0 0 60720 315380 1 3 4 60761 315656 1 529 9 63870 296261 1 0 0 60720 315380 1 405 7 60845 336425 1 972 34 71642 359335 1 0 0 60720 315380 1 2099 64 106611 308636 0 3437 47 48022 158492 0 0 0 60720 315380 1 0 0 60720 315380 0 22330 84 79801 711969 0 0 0 60720 315380 0 0 0 60720 315380 1 483 27 60830 306268 1 0 0 60720 315380 1 2239 21 88590 442882 0 2949 41 82903 378509 1 0 0 60720 315380 1 365 83 87192 346611 1 2461 57 55792 314289 0 21950 519 114337 856956 0 3294 23 75832 217193 1 141 6 61630 315366 1 572 16 58580 307930 1 13326 102 165548 702380 1 2284 33 76403 194493 0 10 2 61656 316155 1 0 0 60720 315380 0 1414 198 70184 330546 1 1975 35 118881 394510 1 43 3 60887 312846 1 0 0 60720 315380 1 844 78 60925 296139 1 304 15 62969 295580 1 458 11 58625 297765 1 18562 155 102313 377934 1 0 0 60720 315380 1 7123 109 288170 638830 1 622 73 73007 304376 1 174 1 64820 307424 1 2220 22 301670 644190 1 121 24 56178 295370 0 11819 85 106113 574339 1 0 0 60720 315380 1 125 12 60798 310201 1 1182 13 70694 327007 1 1503 39 56364 343466 1 0 0 60720 315380 1 0 0 60720 315380 1 30 4 62045 318098 0 3310 33 75230 448243 1 554 11 79285 325738 0 0 0 60720 315380 1 468 21 60720 312161 1 4917 42 52811 243650 1 3256 66 35250 407159 1 0 0 60720 315380 1 125 13 59734 317698 1 0 0 60720 315380 1 22 2 60722 312502 0 0 0 60720 315380 1 514 52 78780 322378 0 0 0 60720 315380 1 0 0 60720 315380 0 0 0 60720 315380 1 0 0 60720 315380 1 0 0 60720 315380 1 0 0 60720 315380 1 10327 91 88577 640273 1 13718 61 96448 345783 1 3748 9 50350 652925 1 14416 361 49857 439798 0 1526 25 69351 278990 0 666 171 117869 339836 1 2844 67 72683 240897 1 0 0 60720 315380 1 368 17 61167 297141 1 0 0 60720 315380 1 333 13 70811 331323 1 26 1 60896 313880 1 0 0 60720 315380 1 0 0 60720 315380 1 1303 61 69863 309422 1 20 3 60938 315245 1 2384 97 61348 405972 0 203 27 50804 300962 0 71 1 60745 316176 1 53 14 59506 302409 1 562 13 58456 283587 1 622 7 60950 263276 1 645 5 60720 312075 0 1763 24 61600 308336 1 0 0 60720 315380 1 317 9 63915 298700 1 1 6 60719 315372 1 275 4 59500 318745 1 0 0 60720 315380 1 936 30 67939 408881 1 8568 37 32168 786690 0 11528 70 -14545 -83265 0 0 0 60720 315380 1 738 10 60720 321376 1 0 0 60720 315380 1 592 7 64270 276898 1 126 10 60951 315547 1 2 1 60743 315487 1 0 0 60720 315380 0 18014 239 -1710 1405225 0 0 0 60720 315380 0 0 0 60720 315380 0 37704 452 60448 983660 0 63 4 65688 318574 1 1431 34 106885 310768 1 94 3 61360 312887 1 192 4 65276 312339 1 32 1 59988 314964 1 6869 105 117520 379983 1 0 0 60720 315380 1 0 0 60720 315380 1 1 4 60722 315398 0 0 0 60720 315380 1 2328 94 82732 253588 0 209 13 64016 316647 1 28 2 60890 315688 1 176 28 68136 310670 1 1920 15 79420 165404 0 87550 458 153198 4111912 1 520 10 58650 291650 0 0 0 60720 315380 1 1013 2 69770 253468 1 15 8 60831 315688 1 587 12 59595 325699 1 5371 66 87720 446211 1 0 0 60720 315380 1 1012 47 114768 368078 1 0 0 60720 315380 1 876 59 138971 352850 0 162556 1081 213118 6282154 1 43556 247 32648 227132 0 3425 12 83620 283910 1 810 43 74015 236761 1 0 0 60720 315380 0 0 0 60720 315380 0 2365 79 191778 550608 0 1261 33 76114 307528 1 0 0 60720 315380 1 1585 107 93099 355864 1 16189 295 116384 358589 1 0 0 60720 315380 0 0 0 60720 315380 1 10579 92 110309 375195 1 474 20 61977 288985 1 0 0 60720 315380 1 3642 86 90262 458343 1 0 0 60720 315380 0 472 20 80045 269587 1 98 2 61490 315236 1 3999 30 51252 42754 1 0 0 60720 315380 1 621 48 60720 308256 1 0 0 60720 315380 1 0 0 60720 315380 0 30 4 60798 313164 1 0 0 60720 315380 1 746 23 71561 269661 0 0 0 60720 315380 1 0 0 60720 315380 0 0 0 60720 315380 1 0 0 60720 315380 0 4150 150 134759 518365 0 4658 158 156608 233773 1 814 18 39625 301881 1 1002 15 56750 298568 0 496 17 87390 325479 1 389 14 58990 325506 1 12679 110 48020 984885 1 400 152 60720 313267 1 53 6 60349 315793 1 0 0 60720 315380 0 5109 97 67038 215362 1 576 18 113761 314073 1 438 11 58320 298096 1 165 24 62841 325176 1 4069 145 79804 207393 0 23 5 62555 314806 1 1285 107 99489 341340 0 4677 75 90131 426280 0 24811 505 95350 929118 1 167 2 64245 307322 1 0 0 60720 315380 1 4628 62 69159 387475 1 226 5 65745 291787 0 1765 20 77623 247060 1 460 15 63346 329784 1 36 3 60894 315834 1 989 11 58930 304555 0 3055 43 60247 376641 0 0 0 60720 315380 1 0 0 60720 315380 0 0 0 60720 315380 1 0 0 60720 315380 1 0 0 60720 315380 1 13253 947 90829 357760 1 24 8 59818 315637 1 0 0 60720 315380 0 0 0 60720 315380 1 0 0 60720 315380 1 0 0 60720 315380 1 131 6 59661 327071 1 514 15 102725 377516 1 3366 80 108479 299243 0 9327 101 87419 387699 1 384 22 54683 309836 1 17821 373 122844 444477 1 397 10 62710 322327 1 897 5 60720 314913 1 218 21 60720 315553 0 3369 28 87161 688779 1 5702 61 101481 321896 1 8636 855 128294 301607 1 534 7 62620 304485 1 0 0 60720 315380 0 0 0 60720 315380 1 726 3 69980 231861 1 1380 26 60982 347385 1 180 11 59635 316386 0 7285 115 188873 491303 1 880 74 80791 261216 1 1 2 60727 315388 1 0 0 60720 315380 1 96 5 60379 313729 1 1889 45 37527 358649 0 45187 353 234817 1926517 1 288 4 60510 296656 1 1270 26 69206 275311 1 6526 26 55830 -42143 0 0 0 60720 315380 1 226 12 72835 343929 1 694 8 68060 367655 1 0 0 60720 315380 1 249 72 56726 313491
Names of X columns:
group costs trades dividends wealth
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
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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