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Data X:
2000 1 501 134 368 6.70 8.50 8.70 2000 2 485 124 361 6.80 8.40 8.60 2000 3 464 113 351 6.70 8.40 8.60 2000 4 460 109 351 6.60 8.30 8.50 2001 5 467 109 358 6.40 8.20 8.50 2001 6 460 106 354 6.30 8.20 8.50 2001 7 448 101 347 6.30 8.10 8.50 2001 8 443 98 345 6.50 8.10 8.50 2001 9 436 93 343 6.50 8.10 8.50 2001 10 431 91 340 6.40 8.10 8.50 2001 11 484 122 362 6.20 8.10 8.50 2001 12 510 139 370 6.20 8.10 8.60 2001 13 513 140 373 6.50 8.10 8.60 2001 14 503 132 371 7.00 8.20 8.60 2001 15 471 117 354 7.20 8.20 8.70 2001 16 471 114 357 7.30 8.30 8.70 2002 17 476 113 363 7.40 8.20 8.70 2002 18 475 110 364 7.40 8.30 8.80 2002 19 470 107 363 7.40 8.30 8.80 2002 20 461 103 358 7.30 8.40 8.90 2002 21 455 98 357 7.40 8.50 8.90 2002 22 456 98 357 7.40 8.50 8.90 2002 23 517 137 380 7.60 8.60 9.00 2002 24 525 148 378 7.60 8.60 9.00 2002 25 523 147 376 7.70 8.70 9.00 2002 26 519 139 380 7.70 8.70 9.00 2002 27 509 130 379 7.80 8.80 9.00 2002 28 512 128 384 7.80 8.80 9.00 2003 29 519 127 392 8.00 8.90 9.10 2003 30 517 123 394 8.10 9.00 9.10 2003 31 510 118 392 8.10 9.00 9.10 2003 32 509 114 396 8.20 9.00 9.10 2003 33 501 108 392 8.10 9.00 9.10 2003 34 507 111 396 8.10 9.10 9.10 2003 35 569 151 419 8.10 9.10 9.10 2003 36 580 159 421 8.10 9.00 9.10 2003 37 578 158 420 8.20 9.10 9.10 2003 38 565 148 418 8.20 9.00 9.10 2003 39 547 138 410 8.30 9.10 9.10 2003 40 555 137 418 8.40 9.10 9.20 2004 41 562 136 426 8.60 9.20 9.30 2004 42 561 133 428 8.60 9.20 9.30 2004 43 555 126 430 8.40 9.20 9.30 2004 44 544 120 424 8.00 9.20 9.20 2004 45 537 114 423 7.90 9.20 9.20 2004 46 543 116 427 8.10 9.30 9.20 2004 47 594 153 441 8.50 9.30 9.20 2004 48 611 162 449 8.80 9.30 9.20 2004 49 613 161 452 8.80 9.30 9.20 2004 50 611 149 462 8.50 9.30 9.20 2004 51 594 139 455 8.30 9.40 9.20 2004 52 595 135 461 8.30 9.40 9.20 2005 53 591 130 461 8.30 9.30 9.20 2005 54 589 127 463 8.40 9.30 9.20 2005 55 584 122 462 8.50 9.30 9.20 2005 56 573 117 456 8.50 9.30 9.20 2005 57 567 112 455 8.60 9.20 9.10 2005 58 569 113 456 8.50 9.20 9.10 2005 59 621 149 472 8.60 9.20 9.00 2005 60 629 157 472 8.60 9.10 8.90 2005 61 628 157 471 8.60 9.10 8.90 2005 62 612 147 465 8.50 9.10 9.00 2005 63 595 137 459 8.40 9.10 8.90 2005 64 597 132 465 8.40 9.00 8.80 2006 65 593 125 468 8.50 8.90 8.70 2006 66 590 123 467 8.50 8.80 8.60 2006 67 580 117 463 8.50 8.70 8.50 2006 68 574 114 460 8.60 8.60 8.50 2006 69 573 111 462 8.60 8.60 8.40 2006 70 573 112 461 8.40 8.50 8.30 2006 71 620 144 476 8.20 8.40 8.20 2006 72 626 150 476 8.00 8.40 8.20 2006 73 620 149 471 8.00 8.30 8.10 2006 74 588 134 453 8.00 8.20 8.00 2006 75 566 123 443 8.00 8.20 7.90 2006 76 557 116 442 7.90 8.00 7.80 2007 77 561 117 444 7.90 7.90 7.60 2007 78 549 111 438 7.90 7.80 7.50 2007 79 532 105 427 7.90 7.70 7.40 2007 80 526 102 424 8.00 7.60 7.30 2007 81 511 95 416 7.90 7.60 7.30 2007 82 499 93 406 7.40 7.60 7.20 2007 83 555 124 431 7.20 7.60 7.20 2007 84 565 130 434 7.00 7.60 7.20 2007 85 542 124 418 6.90 7.50 7.10 2007 86 527 115 412 7.10 7.50 7.00 2007 87 510 106 404 7.20 7.40 7.00 2007 88 514 105 409 7.20 7.40 6.90 2008 89 517 105 412 7.10 7.40 6.90 2008 90 508 101 406 6.90 7.30 6.80 2008 91 493 95 398 6.80 7.30 6.80 2008 92 490 93 397 6.80 7.40 6.80 2008 93 469 84 385 6.80 7.50 6.90 2008 94 478 87 390 6.90 7.60 7.00 2008 95 528 116 413 7.10 7.60 7.00 2008 96 534 120 413 7.20 7.70 7.10 2008 97 518 117 401 7.20 7.70 7.20 2008 98 506 109 397 7.10 7.90 7.30 2008 99 502 105 397 7.10 8.10 7.50 2008 100 516 107 409 7.20 8.40 7.70 2009 101 528 109 419 7.50 8.70 8.10 2009 102 533 109 424 7.70 9.00 8.40 2009 103 536 108 428 7.80 9.30 8.60 2009 104 537 107 430 7.70 9.40 8.80 2009 105 524 99 424 7.70 9.50 8.90 2009 106 536 103 433 7.80 9.60 9.10 2009 107 587 131 456 8.00 9.80 9.20 2009 108 597 137 459 8.10 9.80 9.30 2009 109 581 135 446 8.10 9.90 9.40 2009 110 564 124 441 8.00 10.00 9.40 2009 111 558 118 439 8.10 10.00 9.50 2010 112 575 121 454 8.20 10.10 9.50 2010 113 580 121 460 8.40 10.10 9.70 2010 114 575 118 457 8.50 10.10 9.70 2010 115 563 113 451 8.50 10.10 9.70 2010 116 552 107 444 8.50 10.20 9.70 2010 117 537 100 437 8.50 10.20 9.70 2010 118 545 102 443 8.50 10.10 9.60 2010 119 601 130 471 8.40 10.10 9.60 2010 120 604 136 469 8.30 10.10 9.60 2010 121 586 133 454 8.20 10.10 9.60 2010 122 564 120 444 8.10 10.10 9.60 2010 123 549 112 436 7.90 10.10 9.60 2010 124 551 109 442 7.60 10.10 9.60 2011 125 556 110 446 7.30 10.00 9.50 2011 126 548 106 442 7.10 9.90 9.50 2011 127 540 102 438 7.00 9.90 9.40 2011 128 531 98 433 7.10 9.90 9.40 2011 129 521 92 428 7.10 9.90 9.50 2011 130 519 92 426 7.10 10.00 9.50 2011 131 572 120 452 7.30 10.10 9.60 2011 132 581 127 455 7.30 10.20 9.70 2011 133 563 124 439 7.30 10.30 9.80 2011 134 548 114 434 7.20 10.50 9.90 2011 135 539 108 431 7.20 10.60 10.00 2011 136 541 106 435 7.10 10.70 10.00 2012 137 562 111 450 7.10 10.80 10.10 2012 138 559 110 449 7.10 10.90 10.20 2012 139 546 104 442 7.20 11.00 10.30 2012 140 536 100 437 7.30 11.20 10.30 2012 141 528 96 431 7.40 11.30 10.40 2012 142 530 98 433 7.40 11.40 10.50 2012 143 582 122 460 7.50 11.50 10.50 2012 144 599 134 465 7.40 11.50 10.60 2012 145 584 133 451 7.40 11.60 10.60
Names of X columns:
Jaartal t Totale_werkloosheid Jonger_dan_25 Vanaf_25 Belgiƫ Euroraad EU-27
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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