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Data X:
162556 1081 807 213118 6282154 1 5626 37 56289 29790 309 444 81767 4321023 2 13337 138 28328 87550 458 412 153198 4111912 3 8541 45 17936 84738 588 428 -26007 223193 415 39 0 4145 54660 302 315 126942 1491348 6 4276 24 3040 42634 156 168 157214 1629616 5 9164 34 2964 40949 481 263 129352 1398893 9 2493 29 2865 45187 353 267 234817 1926517 4 4891 38 2854 37704 452 228 60448 983660 14 1734 21 2167 16275 109 129 47818 1443586 7 11409 76 1974 25830 115 104 245546 1073089 10 7592 34 1910 12679 110 122 48020 984885 13 7135 62 1871 18014 239 393 -1710 1405225 8 5043 67 943 43556 247 190 32648 227132 414 110 1 929 24811 505 280 95350 929118 15 1444 29 822 6575 159 63 151352 1071292 11 5480 133 819 7123 109 102 288170 638830 28 4026 62 769 21950 519 265 114337 856956 17 1266 30 745 37597 248 234 37884 992426 12 3195 21 652 17821 373 277 122844 444477 46 655 14 643 12988 119 73 82340 857217 16 5523 51 601 22330 84 67 79801 711969 20 6095 23 446 13326 102 103 165548 702380 21 4925 38 436 16189 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31 70 4901 84 58 121920 469107 41 3204 55 67 2284 33 41 76403 194493 423 -167 -2 66 3160 108 90 108094 530670 33 3062 105 66 4150 150 136 134759 518365 35 2122 77 66 7285 115 97 188873 491303 39 2533 40 59 1134 162 63 146216 527021 34 2019 288 58 4658 158 114 156608 233773 411 214 7 58 2384 97 77 61348 405972 58 2123 86 54 3748 9 6 50350 652925 25 50325 121 53 5371 66 47 87720 446211 45 3730 46 49 1285 107 51 99489 341340 106 1321 110 49 9327 101 85 87419 387699 63 1858 20 44 5565 47 43 94355 493408 38 6243 53 39 1528 38 32 60326 146494 426 -1408 -35 38 3122 34 25 94670 414462 54 6308 69 38 7561 87 77 82425 364304 86 1889 22 37 2675 79 54 59017 355178 93 1964 58 36 13253 947 251 90829 357760 90 167 12 35 880 74 15 80791 261216 399 827 70 34 2053 53 44 100423 397144 60 3720 96 33 1424 94 73 131116 374943 77 1861 123 32 4036 63 85 100269 424898 53 3570 56 32 3045 58 49 27330 202055 422 35 1 31 5119 49 38 39039 378525 69 3643 35 31 1431 34 35 106885 310768 330 3258 77 30 554 11 9 79285 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4993 286 0 365 83 32 87192 346611 99 1766 402 0 49 8 14 64107 322031 137 15254 2500 0 3 4 8 60761 315656 167 28914 37920 0 133 14 4 65990 339445 109 9960 1052 0 32 1 0 59988 314964 301 114964 3603 0 368 17 20 61167 297141 370 5714 264 0 1 6 5 60719 315372 293 19229 84213 0 22 2 1 60722 312502 324 56251 5154 0 96 5 4 60379 313729 316 22746 1188 0 1 2 1 60727 315388 175 57694 117743 0 314 5 4 60720 315371 294 23074 367 0 844 78 20 60925 296139 375 1233 114 0 26 1 1 60896 313880 315 113880 4328 0 125 13 10 59734 317698 153 9054 944 0 304 15 12 62969 295580 376 6372 314 0 621 48 13 60720 308256 341 2255 174 0 119 6 3 59118 303677 354 17280 872 0 1595 17 10 60720 319369 143 7022 75 0 312 14 3 58598 318690 147 8478 381 0 60 10 7 61124 314049 314 11405 1916 0 587 12 10 59595 325699 126 10475 214 0 135 2 1 62065 314210 311 57105 847 0 514 52 15 78780 322378 133 2353 238 0 1 4 4 60722 315398 173 28849 172235 0 1763 24 28 61600 308336 340 4514 61 0 180 11 9 59635 316386 158 10581 647 0 218 21 7 60720 315553 170 5503 531 0 448 40 7 59781 323361 132 3084 275 0 227 9 7 76644 336639 110 15182 603 0 174 1 3 64820 307424 344 107424 618 0 121 24 11 56178 295370 377 3974 788 0 607 11 7 60436 322340 134 11122 201 0 2212 14 10 60720 319864 142 8562 54 0 530 60 18 73433 317291 155 1955 221 0 571 80 14 41477 280398 390 1005 141 0 78 16 12 62700 317330 154 7333 1511 0 2489 40 29 67804 238125 408 953 15 0 131 6 3 59661 327071 123 21179 969 0 923 8 6 58620 309038 337 13630 118 0 72 3 3 60398 314210 312 38070 1591 0 572 16 8 58580 307930 342 6746 189 -1 397 10 10 62710 322327 136 12233 308 -1 450 8 6 59325 292136 380 11517 205 -1 622 7 8 60950 263276 398 9039 102 -2 694 8 6 68060 367655 83 20957 241 -2 3425 12 9 83620 283910 388 6993 25 -3 562 13 8 58456 283587 389 6430 149 -3 4917 42 26 52811 243650 406 1039 9 -3 1442 118 239 121173 438493 49 2021 165 -3 529 9 7 63870 296261 374 10696 182 -3 2126 138 41 21001 230621 413 222 14 -3 1061 5 3 70415 304252 353 20850 98 -4 776 9 8 64230 333505 113 14834 172 -4 611 8 6 59190 296919 371 12115 159 -4 1526 25 21 69351 278990 391 3160 52 -4 592 7 7 64270 276898 392 10985 130 -4 1182 13 11 70694 327007 124 9770 107 -5 621 16 11 68005 317046 156 7315 188 -5 989 11 12 58930 304555 350 9505 106 -5 438 11 9 58320 298096 366 8918 224 -5 726 3 3 69980 231861 412 10620 44 -5 1303 61 57 69863 309422 336 1794 84 -5 7419 29 21 63255 286963 387 2999 12 -5 1164 17 15 57320 269753 395 4103 60 -6 3310 33 32 75230 448243 44 7523 75 -6 1920 15 11 79420 165404 424 -2306 -18 -6 965 3 2 73490 204325 421 1442 4 -8 3256 66 23 35250 407159 57 3139 64 -8 1135 17 20 62285 290476 384 5322 80 -9 1270 26 24 69206 275311 393 2897 59 -10 661 3 1 65920 246541 405 15514 70 -12 1013 2 1 69770 253468 402 26734 53 -14 2844 67 74 72683 240897 407 610 14 -15 11528 70 68 -14545 -83265 431 -4047 -25 -25 6526 26 20 55830 -42143 430 -9313 -37 -28 2264 24 20 55174 272713 394 3030 32 -31 5109 97 82 67038 215362 417 158 3 -31 3999 30 21 51252 42754 428 -5242 -39 -58 35624 223 244 157278 306275 347 477 3 -70 9252 48 32 79510 253537 401 1115 6 -129 15236 90 86 77440 372631 78 1918 11 -158 18073 180 69 27284 -7170 429 -1151 -11 -366
Names of X columns:
Costs trades orders dividends Wealth Wrank Profit/trades Profit/Cost CCscore
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
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3
4
5
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7
8
9
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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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