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Data X:
84738 428 -26007 223193 40949 263 129352 1398893 25830 104 245546 1073089 12679 122 48020 984885 43556 190 32648 227132 6575 63 151352 1071292 7123 102 288170 638830 17821 277 122844 444477 13326 103 165548 702380 16189 290 116384 358589 7146 83 134028 297978 15824 56 63838 585715 11920 73 31080 209458 8568 34 32168 786690 14416 139 49857 439798 2220 12 301670 644190 18562 211 102313 377934 10327 74 88577 640273 4069 131 79804 207393 8636 203 128294 301607 13718 56 96448 345783 4525 89 93811 501749 6869 88 117520 379983 4628 39 69159 387475 4901 58 121920 469107 2284 41 76403 194493 2384 77 61348 405972 3748 6 50350 652925 5371 47 87720 446211 1285 51 99489 341340 1528 32 60326 146494 2675 54 59017 355178 13253 251 90829 357760 880 15 80791 261216 1424 73 131116 374943 5119 38 39039 378525 1431 35 106885 310768 554 9 79285 325738 1975 34 118881 394510 1012 29 114768 368078 810 11 74015 236761 1280 52 69465 312378 1380 29 60982 347385 876 33 138971 352850 814 15 39625 301881 514 15 102725 377516 3642 100 90262 458343 540 13 103960 354228 2099 45 106611 308636 567 14 103345 386212 2001 36 95551 393343 2253 68 63593 452469 1889 43 37527 358649 272 9 112995 429112 2564 19 130140 403560 975 19 90534 235133 3366 55 108479 299243 576 8 113761 314073 1686 28 68696 368186 746 29 71561 269661 5702 47 101481 321896 936 22 67939 408881 5131 44 86111 292154 1503 35 56364 343466 402 8 84990 332743 2239 17 88590 442882 837 21 61262 315688 10579 92 110309 375195 875 12 67000 334280 1585 112 93099 355864 94 10 60793 314533 422 23 57935 318056 34 7 60630 314353 1558 25 55637 369448 43 20 60887 312846 645 4 60720 312075 316 4 60505 315009 115 10 60945 318903 5 1 60720 314887 897 4 60720 314913 389 8 58990 325506 1002 11 56750 298568 36 4 60894 315834 460 15 63346 329784 309 9 56535 312878 9 7 60835 314987 271 2 60720 325249 14 0 61016 315877 520 7 58650 291650 1766 46 60438 305959 458 7 58625 297765 20 2 60938 315245 98 2 61490 315236 405 5 60845 336425 483 7 60830 306268 454 24 63261 302187 47 1 60720 314882 757 18 45689 382712 4655 55 60720 341570 36 3 61564 312412 203 9 61938 309596 126 8 60951 315547 400 113 60720 313267 972 19 71642 359335 2461 25 55792 314289 149 6 62041 313332 226 5 65745 291787 275 7 59500 318745 141 7 61630 315366 28 3 60890 315688 267 11 57640 330059 474 10 61977 288985 534 5 62620 304485 15 6 60831 315688 397 7 60646 317736 1866 28 56225 322331 288 3 60510 296656 3 1 60698 315354 468 20 60720 312161 20 1 60805 315576 278 22 61404 314922 61 9 60720 314551 192 2 65276 312339 317 7 63915 298700 738 9 60720 321376 368 13 61686 303230 2 0 60743 315487 53 6 60349 315793 94 3 61360 312887 24 7 59818 315637 2332 2 72680 324385 131 15 61808 308989 206 9 53110 296702 167 1 64245 307322 622 38 73007 304376 2328 57 82732 253588 365 7 54820 309560 364 26 47705 298466 226 13 72835 343929 307 10 58856 331955 188 9 77655 381180 138 26 69817 331420 125 19 60798 310201 282 12 62452 320016 335 23 64175 320398 176 8 68136 310670 249 26 56726 313491 333 9 70811 331323 30 3 62045 318098 249 13 54323 292754 165 12 62841 325176 453 19 81125 365959 53 10 59506 302409 290 1 58790 301164 366 14 61808 344425 2 12 60735 315394 384 17 54683 309836 365 32 87192 346611 3 8 60761 315656 133 4 65990 339445 32 0 59988 314964 368 20 61167 297141 1 5 60719 315372 22 1 60722 312502 96 4 60379 313729 1 1 60727 315388 314 4 60720 315371 844 20 60925 296139 26 1 60896 313880 125 10 59734 317698 304 12 62969 295580 621 13 60720 308256 119 3 59118 303677 1595 10 60720 319369 312 3 58598 318690 60 7 61124 314049 587 10 59595 325699 135 1 62065 314210 514 15 78780 322378 1 4 60722 315398 180 9 59635 316386 218 7 60720 315553 448 7 59781 323361 227 7 76644 336639 174 3 64820 307424 121 11 56178 295370 607 7 60436 322340 2212 10 60720 319864 530 18 73433 317291 571 14 41477 280398 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 562 8 58456 283587 4917 26 52811 243650 529 7 63870 296261 1061 3 70415 304252 776 8 64230 333505 611 6 59190 296919 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 1920 11 79420 165404 965 2 73490 204325 3256 23 35250 407159 1270 24 69206 275311 661 1 65920 246541 1013 1 69770 253468 2844 74 72683 240897 6526 20 55830 -42143 2264 20 55174 272713 3999 21 51252 42754 35624 244 157278 306275 9252 32 79510 253537 15236 86 77440 372631 18073 69 27284 -7170
Names of X columns:
Costsex Ordersex Dividendsex 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') }
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Raw Input
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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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