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Data X:
84738 428 -26007 -1212617 -2402 -17 40949 263 129352 1367203 2427 29 25830 104 245546 1220017 8870 39 12679 122 48020 984885 7135 62 43556 190 32648 -572920 -3129 -18 6532 62 151352 1151176 9235 146 7123 102 288170 790090 5414 83 17821 277 122844 454195 681 14 13326 103 165548 702380 4925 38 16189 290 116384 264449 218 4 7146 83 134028 450033 2381 35 15824 56 63838 541063 5329 22 11326 64 31080 -37216 -1839 -21 8568 34 32168 783310 15765 68 14416 139 49857 467359 741 19 2220 12 301670 821730 28260 280 18562 211 102313 377934 1148 10 10327 74 88577 651939 4966 44 4069 131 79804 225986 179 6 7710 187 128294 348695 182 19 13718 56 96448 373683 2847 13 4525 89 93811 501709 1335 67 6869 88 117520 413743 2036 31 4628 39 69159 379825 2900 39 4901 58 121920 469107 3204 55 2284 41 76403 211928 361 5 2384 77 61348 423262 2302 94 3748 6 50350 509665 34407 83 5371 47 87720 455881 3877 48 1285 51 99489 367772 1568 131 1528 32 60326 232942 867 22 2675 54 59017 361517 2045 60 13253 251 90829 360962 170 12 880 15 80791 235561 481 40 1424 73 131116 450296 2663 176 5119 38 39039 378519 3643 35 1431 35 106885 326638 3725 89 554 9 79285 328233 11658 232 1975 34 118881 386225 5321 94 1012 29 114768 370225 3622 168 810 11 74015 269236 1610 86 1280 52 69465 365732 1417 129 1380 29 60982 345811 5608 106 876 33 138971 418876 3710 250 814 15 39625 297476 5415 120 514 15 102725 416776 14452 422 3642 100 90262 458343 3004 71 540 13 103960 388386 13456 349 2099 45 106611 358934 2483 76 567 14 103345 407560 18869 366 2001 36 95551 392558 3703 96 2253 68 63593 428370 2307 101 1889 43 37527 358649 3526 84 272 9 112995 467427 33428 985 2564 19 130140 436230 10738 92 975 19 90534 286849 3776 89 3366 55 108479 376685 2209 52 576 8 113761 407198 11511 360 1306 26 68696 377772 6349 136 746 29 71561 271483 3108 96 5477 45 101481 324881 2117 23 936 22 67939 420968 7366 236 5131 44 86111 191521 -119 -2 1503 35 56364 354624 3965 103 402 8 84990 363713 6297 407 2239 17 88590 456657 12222 115 837 21 61262 338381 2006 165 10579 92 110309 418530 2375 21 875 12 67000 351483 10820 173 1395 108 93099 372928 1679 124 94 10 60793 325314 17902 1326 422 23 57935 322046 4359 289 34 7 60630 325599 15700 3668 1558 25 55637 377028 2810 114 43 20 60887 323850 41283 2868 316 4 60505 331514 14613 416 115 10 60945 325632 9664 1096 389 7 58990 322265 8733 314 1002 11 56750 325906 8394 126 36 4 60894 325985 41995 3497 460 15 63346 346145 9743 317 309 9 56535 325898 11445 407 9 7 60835 325356 20893 13581 14 0 61016 325930 125930 8739 520 7 58650 318020 11802 227 1766 46 60438 326389 1731 72 458 7 58625 302925 9357 225 20 2 60938 325540 41847 6181 98 2 61490 326736 63368 1296 405 5 60845 340580 20083 347 483 7 60830 331828 4883 273 454 24 63261 323299 2418 272 757 18 45689 387722 9880 248 36 3 61564 324598 31150 3487 203 9 61938 328726 14303 635 90 6 60951 325043 15630 1394 972 19 71642 387732 5522 193 604 8 55792 332202 3479 219 149 6 62041 328451 25690 862 226 5 65745 307062 21412 474 275 7 59500 331345 32836 477 141 7 61630 331824 21971 936 28 3 60890 325685 62842 4506 267 11 57640 322741 3836 460 474 10 61977 310902 5545 234 534 5 62620 324295 17756 233 15 6 60831 326156 15769 8382 397 7 60646 326960 4534 320 1061 22 56225 333411 6671 126 288 3 60510 297761 24440 339 3 1 60698 325536 62768 39727 20 1 60805 325762 62881 6446 278 22 61404 327957 4921 460 192 2 65276 318521 29630 618 317 7 63915 319775 13308 378 2 0 60743 325486 125486 56781 53 6 60349 325838 20973 2388 94 3 61360 331767 43922 1400 24 7 59818 324523 15565 5139 2332 2 72680 339995 34999 60 131 15 61808 319582 10871 916 206 9 53110 307245 11916 520 167 1 64245 317967 58984 706 622 38 73007 331488 1801 211 885 49 82732 335452 1594 153 365 6 54820 334184 16773 367 364 26 47705 313213 3235 311 226 13 72835 348678 12390 657 307 10 58856 328727 8582 420 188 9 77655 387978 17089 1001 138 26 69817 336704 22784 993 125 19 60798 322076 10173 978 282 12 62452 334272 4476 475 335 23 64175 338197 4188 412 176 8 68136 322145 4362 694 249 26 56726 323351 1713 495 333 9 70811 327748 9827 384 30 3 62045 328157 32039 4245 249 13 54323 311594 1800 449 165 12 62841 335962 5665 825 453 19 81125 372426 8211 380 53 10 59506 319844 8560 2260 290 1 58790 311464 55732 384 366 14 61808 353417 2895 419 2 12 60735 325590 13954 68628 384 17 54683 326126 5733 328 365 32 87192 369376 2041 464 3 8 60761 325871 31468 41269 133 4 65990 342165 10155 1072 32 0 59988 324967 124967 3916 368 20 61167 314832 6755 312 1 5 60719 325557 20926 91647 22 1 60722 322649 61325 5618 96 4 60379 324598 24920 1302 1 1 60727 325567 62784 128130 81 4 60925 324005 17715 1535 26 1 60896 325748 125748 4779 125 10 59734 323385 9491 990 304 12 62969 315409 7694 379 119 3 59118 312275 18713 945 312 3 58598 320576 8613 387 60 7 61124 325246 12525 2104 587 10 59595 332961 11080 227 135 1 62065 323010 61505 912 514 15 78780 345253 2793 283 1 4 60722 325559 31390 187401 180 9 59635 319951 10905 667 448 7 59781 318519 2963 265 227 7 76644 343222 15914 632 174 3 64820 317234 117234 675 121 11 56178 314025 4751 943 607 7 60436 320249 10932 198 530 18 73433 349365 2489 282 571 14 41477 289197 1115 156 78 12 62700 329245 8078 1664 2489 29 67804 240869 1022 16 131 3 59661 327182 21197 970 923 6 58620 322876 15360 133 72 3 60398 323117 41039 1715 572 8 58580 306351 6647 186 397 10 62710 335137 13514 340 450 6 59325 308271 13534 241 622 8 60950 301731 14533 164 694 6 68060 382409 22801 263 562 8 58456 298731 7595 176 4917 26 52811 243650 1039 9 529 7 63870 319771 13308 226 1061 3 70415 347262 29452 139 776 8 64230 343945 15994 186 611 6 59190 311874 13984 183 592 7 64270 316708 16673 197 1182 11 70694 333463 10266 113 621 11 68005 344282 9018 232 989 12 58930 319635 10876 121 438 9 58320 301186 9199 231 726 3 69980 300381 33460 138 1303 57 69863 318765 1947 91 1366 5 79420 312448 16064 82 965 2 73490 299715 33238 103 3256 23 35250 373399 2627 53 1270 24 69206 325586 4830 99 661 1 65920 291221 30407 138 1013 1 69770 261173 30587 60 2844 74 72683 255027 821 19 6526 20 55830 -58143 -9929 -40 2264 20 55174 227033 1126 12 3999 21 51252 21267 -5958 -45 35624 244 157278 238675 173 1 9252 32 79510 197687 -48 0 15236 86 77440 418341 2426 14 18073 69 27284 -297706 -2765 -28
Names of X columns:
Costs Orders 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') }
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Raw Output
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Computing time
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R Server
Big Analytics Cloud Computing Center
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