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Data X:
210907 56 396 81 79 30 120982 56 297 55 58 28 176508 54 559 50 60 38 179321 89 967 125 108 30 123185 40 270 40 49 22 52746 25 143 37 0 26 385534 92 1562 63 121 25 33170 18 109 44 1 18 101645 63 371 88 20 11 149061 44 656 66 43 26 165446 33 511 57 69 25 237213 84 655 74 78 38 173326 88 465 49 86 44 133131 55 525 52 44 30 258873 60 885 88 104 40 180083 66 497 36 63 34 324799 154 1436 108 158 47 230964 53 612 43 102 30 236785 119 865 75 77 31 135473 41 385 32 82 23 202925 61 567 44 115 36 215147 58 639 85 101 36 344297 75 963 86 80 30 153935 33 398 56 50 25 132943 40 410 50 83 39 174724 92 966 135 123 34 174415 100 801 63 73 31 225548 112 892 81 81 31 223632 73 513 52 105 33 124817 40 469 44 47 25 221698 45 683 113 105 33 210767 60 643 39 94 35 170266 62 535 73 44 42 260561 75 625 48 114 43 84853 31 264 33 38 30 294424 77 992 59 107 33 101011 34 238 41 30 13 215641 46 818 69 71 32 325107 99 937 64 84 36 7176 17 70 1 0 0 167542 66 507 59 59 28 106408 30 260 32 33 14 96560 76 503 129 42 17 265769 146 927 37 96 32 269651 67 1269 31 106 30 149112 56 537 65 56 35 175824 107 910 107 57 20 152871 58 532 74 59 28 111665 34 345 54 39 28 116408 61 918 76 34 39 362301 119 1635 715 76 34 78800 42 330 57 20 26 183167 66 557 66 91 39 277965 89 1178 106 115 39 150629 44 740 54 85 33 168809 66 452 32 76 28 24188 24 218 20 8 4 329267 259 764 71 79 39 65029 17 255 21 21 18 101097 64 454 70 30 14 218946 41 866 112 76 29 244052 68 574 66 101 44 341570 168 1276 190 94 21 103597 43 379 66 27 16 233328 132 825 165 92 28 256462 105 798 56 123 35 206161 71 663 61 75 28 311473 112 1069 53 128 38 235800 94 921 127 105 23 177939 82 858 63 55 36 207176 70 711 38 56 32 196553 57 503 50 41 29 174184 53 382 52 72 25 143246 103 464 42 67 27 187559 121 717 76 75 36 187681 62 690 67 114 28 119016 52 462 50 118 23 182192 52 657 53 77 40 73566 32 385 39 22 23 194979 62 577 50 66 40 167488 45 619 77 69 28 143756 46 479 57 105 34 275541 63 817 73 116 33 243199 75 752 34 88 28 182999 88 430 39 73 34 135649 46 451 46 99 30 152299 53 537 63 62 33 120221 37 519 35 53 22 346485 90 1000 106 118 38 145790 63 637 43 30 26 193339 78 465 47 100 35 80953 25 437 31 49 8 122774 45 711 162 24 24 130585 46 299 57 67 29 112611 41 248 36 46 20 286468 144 1162 263 57 29 241066 82 714 78 75 45 148446 91 905 63 135 37 204713 71 649 54 68 33 182079 63 512 63 124 33 140344 53 472 77 33 25 220516 62 905 79 98 32 243060 63 786 110 58 29 162765 32 489 56 68 28 182613 39 479 56 81 28 232138 62 617 43 131 31 265318 117 925 111 110 52 85574 34 351 71 37 21 310839 92 1144 62 130 24 225060 93 669 56 93 41 232317 54 707 74 118 33 144966 144 458 60 39 32 43287 14 214 43 13 19 155754 61 599 68 74 20 164709 109 572 53 81 31 201940 38 897 87 109 31 235454 73 819 46 151 32 220801 75 720 105 51 18 99466 50 273 32 28 23 92661 61 508 133 40 17 133328 55 506 79 56 20 61361 77 451 51 27 12 125930 75 699 207 37 17 100750 72 407 67 83 30 224549 50 465 47 54 31 82316 32 245 34 27 10 102010 53 370 66 28 13 101523 42 316 76 59 22 243511 71 603 65 133 42 22938 10 154 9 12 1 41566 35 229 42 0 9 152474 65 577 45 106 32 61857 25 192 25 23 11 99923 66 617 115 44 25 132487 41 411 97 71 36 317394 86 975 53 116 31 21054 16 146 2 4 0 209641 42 705 52 62 24 22648 19 184 44 12 13 31414 19 200 22 18 8 46698 45 274 35 14 13 131698 65 502 74 60 19 91735 35 382 103 7 18 244749 95 964 144 98 33 184510 49 537 60 64 40 79863 37 438 134 29 22 128423 64 369 89 32 38 97839 38 417 42 25 24 38214 34 276 52 16 8 151101 32 514 98 48 35 272458 65 822 99 100 43 172494 52 389 52 46 43 108043 62 466 29 45 14 328107 65 1255 125 129 41 250579 83 694 106 130 38 351067 95 1024 95 136 45 158015 29 400 40 59 31 98866 18 397 140 25 13 85439 33 350 43 32 28 229242 247 719 128 63 31 351619 139 1277 142 95 40 84207 29 356 73 14 30 120445 118 457 72 36 16 324598 110 1402 128 113 37 131069 67 600 61 47 30 204271 42 480 73 92 35 165543 65 595 148 70 32 141722 94 436 64 19 27 116048 64 230 45 50 20 250047 81 651 58 41 18 299775 95 1367 97 91 31 195838 67 564 50 111 31 173260 63 716 37 41 21 254488 83 747 50 120 39 104389 45 467 105 135 41 136084 30 671 69 27 13 199476 70 861 46 87 32 92499 32 319 57 25 18 224330 83 612 52 131 39 135781 31 433 98 45 14 74408 67 434 61 29 7 81240 66 503 89 58 17 14688 10 85 0 4 0 181633 70 564 48 47 30 271856 103 824 91 109 37 7199 5 74 0 7 0 46660 20 259 7 12 5 17547 5 69 3 0 1 133368 36 535 54 37 16 95227 34 239 70 37 32 152601 48 438 36 46 24 98146 40 459 37 15 17 79619 43 426 123 42 11 59194 31 288 247 7 24 139942 42 498 46 54 22 118612 46 454 72 54 12 72880 33 376 41 14 19 65475 18 225 24 16 13 99643 55 555 45 33 17 71965 35 252 33 32 15 77272 59 208 27 21 16 49289 19 130 36 15 24 135131 66 481 87 38 15 108446 60 389 90 22 17 89746 36 565 114 28 18 44296 25 173 31 10 20 77648 47 278 45 31 16 181528 54 609 69 32 16 134019 53 422 51 32 18 124064 40 445 34 43 22 92630 40 387 60 27 8 121848 39 339 45 37 17 52915 14 181 54 20 18 81872 45 245 25 32 16 58981 36 384 38 0 23 53515 28 212 52 5 22 60812 44 399 67 26 13 56375 30 229 74 10 13 65490 22 224 38 27 16 80949 17 203 30 11 16 76302 31 333 26 29 20 104011 55 384 67 25 22 98104 54 636 132 55 17 67989 21 185 42 23 18 30989 14 93 35 5 17 135458 81 581 118 43 12 73504 35 248 68 23 7 63123 43 304 43 34 17 61254 46 344 76 36 14 74914 30 407 64 35 23 31774 23 170 48 0 17 81437 38 312 64 37 14 87186 54 507 56 28 15 50090 20 224 71 16 17 65745 53 340 75 26 21 56653 45 168 39 38 18 158399 39 443 42 23 18 46455 20 204 39 22 17 73624 24 367 93 30 17 38395 31 210 38 16 16 91899 35 335 60 18 15 139526 151 364 71 28 21 52164 52 178 52 32 16 51567 30 206 27 21 14 70551 31 279 59 23 15 84856 29 387 40 29 17 102538 57 490 79 50 15 86678 40 238 44 12 15 85709 44 343 65 21 10 34662 25 232 10 18 6 150580 77 530 124 27 22 99611 35 291 81 41 21 19349 11 67 15 13 1 99373 63 397 92 12 18 86230 44 467 42 21 17 30837 19 178 10 8 4 31706 13 175 24 26 10 89806 42 299 64 27 16 62088 38 154 45 13 16 40151 29 106 22 16 9 27634 20 189 56 2 16 76990 27 194 94 42 17 37460 20 135 19 5 7 54157 19 201 35 37 15 49862 37 207 32 17 14 84337 26 280 35 38 14 64175 42 260 48 37 18 59382 49 227 49 29 12 119308 30 239 48 32 16 76702 49 333 62 35 21 103425 67 428 96 17 19 70344 28 230 45 20 16 43410 19 292 63 7 1 104838 49 350 71 46 16 62215 27 186 26 24 10 69304 30 326 48 40 19 53117 22 155 29 3 12 19764 12 75 19 10 2 86680 31 361 45 37 14 84105 20 261 45 17 17 77945 20 299 67 28 19 89113 39 300 30 19 14 91005 29 450 36 29 11 40248 16 183 34 8 4 64187 27 238 36 10 16 50857 21 165 34 15 20 56613 19 234 37 15 12 62792 35 176 46 28 15 72535 14 329 44 17 16
Names of X columns:
TIRFC NOL CVI CVPR BC CR
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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