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Data X:
56 396 81 3 79 30 115 56 297 55 4 58 28 109 54 559 50 12 60 38 146 89 967 125 2 108 30 116 40 270 40 1 49 22 68 25 143 37 3 0 26 101 92 1562 63 0 121 25 96 18 109 44 0 1 18 67 63 371 88 0 20 11 44 44 656 66 5 43 26 100 33 511 57 0 69 25 93 84 655 74 0 78 38 140 88 465 49 7 86 44 166 55 525 52 7 44 30 99 60 885 88 3 104 40 139 66 497 36 9 63 34 130 154 1436 108 0 158 47 181 53 612 43 4 102 30 116 119 865 75 3 77 31 116 41 385 32 0 82 23 88 61 567 44 7 115 36 139 58 639 85 0 101 36 135 75 963 86 1 80 30 108 33 398 56 5 50 25 89 40 410 50 7 83 39 156 92 966 135 0 123 34 129 100 801 63 0 73 31 118 112 892 81 5 81 31 118 73 513 52 0 105 33 125 40 469 44 0 47 25 95 45 683 113 0 105 33 126 60 643 39 3 94 35 135 62 535 73 4 44 42 154 75 625 48 1 114 43 165 31 264 33 4 38 30 113 77 992 59 2 107 33 127 34 238 41 0 30 13 52 46 818 69 0 71 32 121 99 937 64 0 84 36 136 17 70 1 0 0 0 0 66 507 59 2 59 28 108 30 260 32 1 33 14 46 76 503 129 0 42 17 54 146 927 37 2 96 32 124 67 1269 31 10 106 30 115 56 537 65 6 56 35 128 107 910 107 0 57 20 80 58 532 74 5 59 28 97 34 345 54 4 39 28 104 61 918 76 1 34 39 59 119 1635 715 2 76 34 125 42 330 57 2 20 26 82 66 557 66 0 91 39 149 89 1178 106 8 115 39 149 44 740 54 3 85 33 122 66 452 32 0 76 28 118 24 218 20 0 8 4 12 259 764 71 8 79 39 144 17 255 21 5 21 18 67 64 454 70 3 30 14 52 41 866 112 1 76 29 108 68 574 66 5 101 44 166 168 1276 190 1 94 21 80 43 379 66 1 27 16 60 132 825 165 5 92 28 107 105 798 56 0 123 35 127 71 663 61 12 75 28 107 112 1069 53 8 128 38 146 94 921 127 8 105 23 84 82 858 63 8 55 36 141 70 711 38 8 56 32 123 57 503 50 2 41 29 111 53 382 52 0 72 25 98 103 464 42 5 67 27 105 121 717 76 8 75 36 135 62 690 67 2 114 28 107 52 462 50 5 118 23 85 52 657 53 12 77 40 155 32 385 39 6 22 23 88 62 577 50 7 66 40 155 45 619 77 2 69 28 104 46 479 57 0 105 34 132 63 817 73 4 116 33 127 75 752 34 3 88 28 108 88 430 39 6 73 34 129 46 451 46 2 99 30 116 53 537 63 0 62 33 122 37 519 35 1 53 22 85 90 1000 106 0 118 38 147 63 637 43 5 30 26 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7 28 66 503 89 0 58 17 68 10 85 0 0 4 0 0 70 564 48 2 47 30 110 103 824 91 1 109 37 147 5 74 0 0 7 0 0 20 259 7 0 12 5 15 5 69 3 0 0 1 4 36 535 54 1 37 16 64 34 239 70 0 37 32 111 48 438 36 2 46 24 85 40 459 37 0 15 17 68 43 426 123 3 42 11 40 31 288 247 6 7 24 80 42 498 46 0 54 22 88 46 454 72 2 54 12 48 33 376 41 0 14 19 76 18 225 24 2 16 13 51 55 555 45 1 33 17 67 35 252 33 1 32 15 59 59 208 27 2 21 16 61 19 130 36 1 15 24 76 66 481 87 0 38 15 60 60 389 90 1 22 17 68 36 565 114 3 28 18 71 25 173 31 0 10 20 76 47 278 45 0 31 16 62 54 609 69 0 32 16 61 53 422 51 0 32 18 67 40 445 34 1 43 22 88 40 387 60 4 27 8 30 39 339 45 0 37 17 64 14 181 54 0 20 18 68 45 245 25 0 32 16 64 36 384 38 7 0 23 91 28 212 52 2 5 22 88 44 399 67 0 26 13 52 30 229 74 7 10 13 49 22 224 38 3 27 16 62 17 203 30 0 11 16 61 31 333 26 0 29 20 76 55 384 67 6 25 22 88 54 636 132 2 55 17 66 21 185 42 0 23 18 71 14 93 35 0 5 17 68 81 581 118 3 43 12 48 35 248 68 0 23 7 25 43 304 43 1 34 17 68 46 344 76 1 36 14 41 30 407 64 0 35 23 90 23 170 48 1 0 17 66 38 312 64 0 37 14 54 54 507 56 0 28 15 59 20 224 71 0 16 17 60 53 340 75 0 26 21 77 45 168 39 0 38 18 68 39 443 42 0 23 18 72 20 204 39 0 22 17 67 24 367 93 0 30 17 64 31 210 38 0 16 16 63 35 335 60 0 18 15 59 151 364 71 0 28 21 84 52 178 52 0 32 16 64 30 206 27 2 21 14 56 31 279 59 0 23 15 54 29 387 40 1 29 17 67 57 490 79 1 50 15 58 40 238 44 0 12 15 59 44 343 65 0 21 10 40 25 232 10 0 18 6 22 77 530 124 0 27 22 83 35 291 81 0 41 21 81 11 67 15 0 13 1 2 63 397 92 1 12 18 72 44 467 42 0 21 17 61 19 178 10 0 8 4 15 13 175 24 0 26 10 32 42 299 64 0 27 16 62 38 154 45 1 13 16 58 29 106 22 0 16 9 36 20 189 56 0 2 16 59 27 194 94 0 42 17 68 20 135 19 0 5 7 21 19 201 35 0 37 15 55 37 207 32 0 17 14 54 26 280 35 0 38 14 55 42 260 48 0 37 18 72 49 227 49 0 29 12 41 30 239 48 0 32 16 61 49 333 62 0 35 21 67 67 428 96 1 17 19 76 28 230 45 0 20 16 64 19 292 63 0 7 1 3 49 350 71 1 46 16 63 27 186 26 0 24 10 40 30 326 48 6 40 19 69 22 155 29 3 3 12 48 12 75 19 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Names of X columns:
logins compendium_views_info compendium_views_pr shared_compendiums blogged_computations compendiums_reviewed feedback_messages_p1
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
0 seconds
R Server
Big Analytics Cloud Computing Center
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