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Data X:
501 134 368 6.7 8.5 8.7 485 124 361 6.8 8.4 8.6 464 113 351 6.7 8.4 8.6 460 109 351 6.6 8.3 8.5 467 109 358 6.4 8.2 8.5 460 106 354 6.3 8.2 8.5 448 101 347 6.3 8.1 8.5 443 98 345 6.5 8.1 8.5 436 93 343 6.5 8.1 8.5 431 91 340 6.4 8.1 8.5 484 122 362 6.2 8.1 8.5 510 139 370 6.2 8.1 8.6 513 140 373 6.5 8.1 8.6 503 132 371 7 8.2 8.6 471 117 354 7.2 8.2 8.7 471 114 357 7.3 8.3 8.7 476 113 363 7.4 8.2 8.7 475 110 364 7.4 8.3 8.8 470 107 363 7.4 8.3 8.8 461 103 358 7.3 8.4 8.9 455 98 357 7.4 8.5 8.9 456 98 357 7.4 8.5 8.9 517 137 380 7.6 8.6 9 525 148 378 7.6 8.6 9 523 147 376 7.7 8.7 9 519 139 380 7.7 8.7 9 509 130 379 7.8 8.8 9 512 128 384 7.8 8.8 9 519 127 392 8 8.9 9.1 517 123 394 8.1 9 9.1 510 118 392 8.1 9 9.1 509 114 396 8.2 9 9.1 501 108 392 8.1 9 9.1 507 111 396 8.1 9.1 9.1 569 151 419 8.1 9.1 9.1 580 159 421 8.1 9 9.1 578 158 420 8.2 9.1 9.1 565 148 418 8.2 9 9.1 547 138 410 8.3 9.1 9.1 555 137 418 8.4 9.1 9.2 562 136 426 8.6 9.2 9.3 561 133 428 8.6 9.2 9.3 555 126 430 8.4 9.2 9.3 544 120 424 8 9.2 9.2 537 114 423 7.9 9.2 9.2 543 116 427 8.1 9.3 9.2 594 153 441 8.5 9.3 9.2 611 162 449 8.8 9.3 9.2 613 161 452 8.8 9.3 9.2 611 149 462 8.5 9.3 9.2 594 139 455 8.3 9.4 9.2 595 135 461 8.3 9.4 9.2 591 130 461 8.3 9.3 9.2 589 127 463 8.4 9.3 9.2 584 122 462 8.5 9.3 9.2 573 117 456 8.5 9.3 9.2 567 112 455 8.6 9.2 9.1 569 113 456 8.5 9.2 9.1 621 149 472 8.6 9.2 9 629 157 472 8.6 9.1 8.9 628 157 471 8.6 9.1 8.9 612 147 465 8.5 9.1 9 595 137 459 8.4 9.1 8.9 597 132 465 8.4 9 8.8 593 125 468 8.5 8.9 8.7 590 123 467 8.5 8.8 8.6 580 117 463 8.5 8.7 8.5 574 114 460 8.6 8.6 8.5 573 111 462 8.6 8.6 8.4 573 112 461 8.4 8.5 8.3 620 144 476 8.2 8.4 8.2 626 150 476 8 8.4 8.2 620 149 471 8 8.3 8.1 588 134 453 8 8.2 8 566 123 443 8 8.2 7.9 557 116 442 7.9 8 7.8 561 117 444 7.9 7.9 7.6 549 111 438 7.9 7.8 7.5 532 105 427 7.9 7.7 7.4 526 102 424 8 7.6 7.3 511 95 416 7.9 7.6 7.3 499 93 406 7.4 7.6 7.2 555 124 431 7.2 7.6 7.2 565 130 434 7 7.6 7.2 542 124 418 6.9 7.5 7.1 527 115 412 7.1 7.5 7 510 106 404 7.2 7.4 7 514 105 409 7.2 7.4 6.9 517 105 412 7.1 7.4 6.9 508 101 406 6.9 7.3 6.8 493 95 398 6.8 7.3 6.8 490 93 397 6.8 7.4 6.8 469 84 385 6.8 7.5 6.9 478 87 390 6.9 7.6 7 528 116 413 7.1 7.6 7 534 120 413 7.2 7.7 7.1 518 117 401 7.2 7.7 7.2 506 109 397 7.1 7.9 7.3 502 105 397 7.1 8.1 7.5 516 107 409 7.2 8.4 7.7 528 109 419 7.5 8.7 8.1 533 109 424 7.7 9 8.4 536 108 428 7.8 9.3 8.6 537 107 430 7.7 9.4 8.8 524 99 424 7.7 9.5 8.9 536 103 433 7.8 9.6 9.1 587 131 456 8 9.8 9.2 597 137 459 8.1 9.8 9.3 581 135 446 8.1 9.9 9.4 564 124 441 8 10 9.4 558 118 439 8.1 10 9.5 575 121 454 8.2 10.1 9.5 580 121 460 8.4 10.1 9.7 575 118 457 8.5 10.1 9.7 563 113 451 8.5 10.1 9.7 552 107 444 8.5 10.2 9.7 537 100 437 8.5 10.2 9.7 545 102 443 8.5 10.1 9.6 601 130 471 8.4 10.1 9.6 604 136 469 8.3 10.1 9.6 586 133 454 8.2 10.1 9.6 564 120 444 8.1 10.1 9.6 549 112 436 7.9 10.1 9.6 551 109 442 7.6 10.1 9.6 556 110 446 7.3 10 9.5 548 106 442 7.1 9.9 9.5 540 102 438 7 9.9 9.4 531 98 433 7.1 9.9 9.4 521 92 428 7.1 9.9 9.5 519 92 426 7.1 10 9.5 572 120 452 7.3 10.1 9.6 581 127 455 7.3 10.2 9.7 563 124 439 7.3 10.3 9.8 548 114 434 7.2 10.5 9.9 539 108 431 7.2 10.6 10 541 106 435 7.1 10.7 10 562 111 450 7.1 10.8 10.1 559 110 449 7.1 10.9 10.2 546 104 442 7.2 11 10.3 536 100 437 7.3 11.2 10.3 528 96 431 7.4 11.3 10.4 530 98 433 7.4 11.4 10.5 582 122 460 7.5 11.5 10.5 599 134 465 7.4 11.5 10.6 584 133 451 7.4 11.6 10.6
Names of X columns:
totaal <25J >25J belgie eurogebied eu
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') }
Compute
Summary of computational transaction
Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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