Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
162556 1081 213118 6282929 5627 37 29790 309 81767 4324047 13346 138 87550 458 153198 4108272 8533 45 84738 588 -26007 -1212617 -2402 -17 54660 299 126942 1485329 4299 24 42634 156 157214 1779876 10127 37 40949 481 129352 1367203 2427 29 42312 323 234817 2519076 7180 55 37704 452 60448 912684 1577 19 16275 109 47818 1443586 11409 76 25830 115 245546 1220017 8870 39 12679 110 48020 984885 7135 62 18014 239 -1710 1457425 5261 70 43556 247 32648 -572920 -3129 -18 24524 497 95350 929144 1467 30 6532 103 151352 1151176 9235 146 7123 109 288170 790090 5414 83 20813 502 114337 774497 1144 28 37597 248 37884 990576 3188 21 17821 373 122844 454195 681 14 12988 119 82340 876607 5686 52 22330 84 79801 711969 6095 23 13326 102 165548 702380 4925 38 16189 295 116384 264449 218 4 7146 105 134028 450033 2381 35 15824 64 63838 541063 5329 22 26088 267 74996 588864 1456 15 11326 129 31080 -37216 -1839 -21 8568 37 32168 783310 15765 68 14416 361 49857 467359 741 19 3369 28 87161 688779 17456 145 11819 85 106113 608419 4805 35 6620 44 80570 696348 11281 75 4519 49 102129 597793 8118 88 2220 22 301670 821730 28260 280 18562 155 102313 377934 1148 10 10327 91 88577 651939 4966 44 5336 81 112477 697458 6141 93 2365 79 191778 700368 6334 212 4069 145 79804 225986 179 6 7710 816 128294 348695 182 19 13718 61 96448 373683 2847 13 4525 226 93811 501709 1335 67 6869 105 117520 413743 2036 31 4628 62 69159 379825 2900 39 3653 24 101792 336260 5677 37 1265 26 210568 636765 16799 345 7489 322 136996 481231 873 38 4901 84 121920 469107 3204 55 2284 33 76403 211928 361 5 3160 108 108094 563925 3370 115 4150 150 134759 511939 2080 75 7285 115 188873 521016 2791 44 1134 162 146216 543856 2123 303 4658 158 156608 329304 818 28 2384 97 61348 423262 2302 94 3748 9 50350 509665 34407 83 5371 66 87720 455881 3877 48 1285 107 99489 367772 1568 131 9327 101 87419 406339 2043 22 5565 47 94355 493408 6243 53 1528 38 60326 232942 867 22 3122 34 94670 416002 6353 69 7317 84 82425 337430 1636 19 2675 79 59017 361517 2045 60 13253 947 90829 360962 170 12 880 74 80791 235561 481 40 2053 53 100423 408247 3929 101 1424 94 131116 450296 2663 176 4036 63 100269 418799 3473 54 3045 58 27330 247405 817 16 5119 49 39039 378519 3643 35 1431 34 106885 326638 3725 89 554 11 79285 328233 11658 232 1975 35 118881 386225 5321 94 1286 17 77623 283662 4921 65 1012 47 114768 370225 3622 168 810 43 74015 269236 1610 86 1280 117 69465 365732 1417 129 666 171 117869 420383 1289 331 1380 26 60982 345811 5608 106 4608 73 90131 431809 3175 50 876 59 138971 418876 3710 250 814 18 39625 297476 5415 120 514 15 102725 416776 14452 422 5692 72 64239 357257 2184 28 3642 86 90262 458343 3004 71 540 14 103960 388386 13456 349 2099 64 106611 358934 2483 76 567 11 103345 407560 18869 366 2001 52 95551 392558 3703 96 2949 41 82903 373177 4224 59 2253 99 63593 428370 2307 101 6533 75 126910 369419 2259 26 1889 45 37527 358649 3526 84 3055 43 60247 376641 4108 58 272 8 112995 467427 33428 985 1414 198 70184 364885 833 117 2564 22 130140 436230 10738 92 1383 11 73221 329118 11738 93 1261 33 76114 317365 3557 93 975 23 90534 286849 3776 89 3366 80 108479 376685 2209 52 576 18 113761 407198 11511 360 1306 28 68696 377772 6349 136 746 23 71561 271483 3108 96 3192 60 59831 153661 -772 -15 2045 20 97890 513294 15665 153 5477 59 101481 324881 2117 23 1932 36 72954 264512 1792 33 936 30 67939 420968 7366 236 3437 47 48022 129302 -1504 -21 5131 71 86111 191521 -119 -2 2397 14 74020 268673 4905 29 1389 9 57530 353179 17020 110 1503 39 56364 354624 3965 103 402 26 84990 363713 6297 407 2239 21 88590 456657 12222 115 2234 16 77200 211742 734 5 837 69 61262 338381 2006 165 10579 92 110309 418530 2375 21 875 14 67000 351483 10820 173 1395 103 93099 372928 1679 124 1659 29 107577 485538 9846 172 2647 37 62920 279268 2142 30 3294 23 75832 219060 829 6 94 7 60793 325314 17902 1326 422 28 57935 322046 4359 289 34 8 60630 325599 15700 3668 1558 63 55637 377028 2810 114 43 3 60887 323850 41283 2868 316 9 60505 331514 14613 416 115 13 60945 325632 9664 1096 389 14 58990 322265 8733 314 1002 15 56750 325906 8394 126 36 3 60894 325985 41995 3497 460 15 63346 346145 9743 317 309 11 56535 325898 11445 407 9 6 60835 325356 20893 13581 14 1 61016 325930 125930 8739 520 10 58650 318020 11802 227 1766 73 60438 326389 1731 72 458 11 58625 302925 9357 225 20 3 60938 325540 41847 6181 98 2 61490 326736 63368 1296 405 7 60845 340580 20083 347 483 27 60830 331828 4883 273 454 51 63261 323299 2418 272 757 19 45689 387722 9880 248 36 4 61564 324598 31150 3487 203 9 61938 328726 14303 635 90 8 60951 325043 15630 1394 71 1 60745 325806 125806 1782 972 34 71642 387732 5522 193 531 10 71641 349729 14973 282 604 38 55792 332202 3479 219 283 10 71873 305442 10544 372 23 5 62555 329537 25907 5613 638 14 60370 327055 9075 199 699 16 64873 356245 9765 224 149 5 62041 328451 25690 862 226 5 65745 307062 21412 474 275 4 59500 331345 32836 477 141 6 61630 331824 21971 936 28 2 60890 325685 62842 4506 2566 80 113521 404480 2556 80 472 20 80045 318314 5916 251 203 27 50804 311807 4141 551 496 17 87390 337724 8101 277 10 2 61656 326431 63215 12668 63 4 65688 327556 31889 2034 1136 32 48522 356850 4902 138 267 32 57640 322741 3836 460 474 20 61977 310902 5545 234 534 7 62620 324295 17756 233 15 8 60831 326156 15769 8382 397 28 60646 326960 4534 320 1061 20 56225 333411 6671 126 288 4 60510 297761 24440 339 3 2 60698 325536 62768 39727 20 2 60805 325762 62881 6446 278 26 61404 327957 4921 460 192 4 65276 318521 29630 618 317 9 63915 319775 13308 378 2 1 60743 325486 125486 56781 53 6 60349 325838 20973 2388 94 3 61360 331767 43922 1400 24 8 59818 324523 15565 5139 2332 4 72680 339995 34999 60 131 11 61808 319582 10871 916 206 9 53110 307245 11916 520 167 2 64245 317967 58984 706 622 73 73007 331488 1801 211 885 85 82732 335452 1594 153 365 8 54820 334184 16773 367 364 35 47705 313213 3235 311 226 12 72835 348678 12390 657 307 15 58856 328727 8582 420 188 11 77655 387978 17089 1001 138 6 69817 336704 22784 993 125 12 60798 322076 10173 978 282 30 62452 334272 4476 475 335 33 64175 338197 4188 412 813 82 67440 321024 1476 149 176 28 68136 322145 4362 694 249 72 56726 323351 1713 495 333 13 70811 327748 9827 384 30 4 62045 328157 32039 4245 249 62 54323 311594 1800 449 165 24 62841 335962 5665 825 453 21 81125 372426 8211 380 53 14 59506 319844 8560 2260 382 21 59365 355822 7420 407 30 4 60798 324047 31012 4174 290 2 58790 311464 55732 384 366 53 61808 353417 2895 419 2 9 60735 325590 13954 68628 209 13 64016 328576 9890 615 384 22 54683 326126 5733 328 365 83 87192 369376 2041 464 49 8 64107 332013 16502 2704 3 4 60761 325871 31468 41269 133 14 65990 342165 10155 1072 32 1 59988 324967 124967 3916 368 17 61167 314832 6755 312 1 6 60719 325557 20926 91647 22 2 60722 322649 61325 5618 96 5 60379 324598 24920 1302 1 2 60727 325567 62784 128130 81 7 60925 324005 17715 1535 26 1 60896 325748 125748 4779 125 13 59734 323385 9491 990 304 15 62969 315409 7694 379 119 6 59118 312275 18713 945 312 14 58598 320576 8613 387 60 10 61124 325246 12525 2104 587 12 59595 332961 11080 227 135 2 62065 323010 61505 912 514 52 78780 345253 2793 283 1 4 60722 325559 31390 187401 58 3 61600 319634 39878 2072 180 11 59635 319951 10905 667 448 40 59781 318519 2963 265 227 9 76644 343222 15914 632 174 1 64820 317234 117234 675 121 24 56178 314025 4751 943 607 11 60436 320249 10932 198 530 60 73433 349365 2489 282 571 80 41477 289197 1115 156 78 16 62700 329245 8078 1664 2489 40 67804 240869 1022 16 131 6 59661 327182 21197 970 923 8 58620 322876 15360 133 72 3 60398 323117 41039 1715 572 16 58580 306351 6647 186 397 10 62710 335137 13514 340 450 8 59325 308271 13534 241 622 7 60950 301731 14533 164 694 8 68060 382409 22801 263 3425 12 83620 279230 6603 23 562 13 58456 298731 7595 176 4917 42 52811 243650 1039 9 1442 118 121173 532682 2819 231 529 9 63870 319771 13308 226 2126 138 21001 171493 -207 -13 1061 5 70415 347262 29452 139 776 9 64230 343945 15994 186 611 8 59190 311874 13984 183 1526 25 69351 302211 4088 67 592 7 64270 316708 16673 197 1182 13 70694 333463 10266 113 621 16 68005 344282 9018 232 989 11 58930 319635 10876 121 438 11 58320 301186 9199 231 726 3 69980 300381 33460 138 1303 61 69863 318765 1947 91 6341 24 63255 286146 3589 14 1164 17 57320 306844 6285 92 3310 33 75230 307705 3264 33 1366 7 79420 312448 16064 82 965 3 73490 299715 33238 103 3256 66 35250 373399 2627 53 1135 17 62285 299446 5850 88 1270 26 69206 325586 4830 99 661 3 65920 291221 30407 138 1013 2 69770 261173 30587 60 2844 67 72683 255027 821 19 11528 70 -14545 -78375 -3977 -24 6526 26 55830 -58143 -9929 -40 2264 24 55174 227033 1126 12 4461 94 67038 235098 373 8 3999 30 51252 21267 -5958 -45 35624 223 157278 238675 173 1 9252 48 79510 197687 -48 0 15236 90 77440 418341 2426 14 18073 180 27284 -297706 -2765 -28
Names of X columns:
Costs Trades Dividends Wealth Profit/Trades Profit/Cost
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