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Data X:
1.594.385 150.477 59 32 878.114 107.218 2.110 1.106 13.866 424 59 15 404.149 79.356 65 51 12.442 307 36 30 15.506 1.203 134 94 9.565 560 109 46 33.743 3.206 92 62 26.290 1.307 88 33 20.270 901 33 19 14.151 3.646 21 15 9.849 317 61 33 8.028 171 101 57 15.776 1.955 75 50 24.175 2.252 37 16 19.853 636 83 58 8.182 181 46 19 23.517 1.220 64 38 8.971 297 61 28 17.921 332 21 14 14.325 287 49 45 7.930 174 158 84 17.785 1.711 93 42 31.483 1.859 47 18 17.360 606 44 35 8.682 302 82 42 11.894 440 52 25 17.845 1.368 69 48 12.114 273 84 42 20.515 750 59 18 17.930 643 42 34 13.997 534 37 24 311.275 13.036 79 51 10.710 179 76 45 14.309 360 144 101 20.199 357 178 84 16.152 425 380 206 39.223 643 87 45 32.572 1.358 56 34 73.917 7.023 54 35 21.287 285 36 14 16.082 270 75 45 16.275 263 89 65 7.869 200 51 28 19.605 544 7 2 23.075 1.129 78 49 404.996 30.223 79 39 11.138 1.620 31 22 1.317 1.187 158 72 20.134 888 30 21 16.197 806 115 76 8.707 400 31 20 35.435 1.555 57 45 10.727 248 62 34 25.906 853 47 27 8.524 179 41 37 13.959 288 69 35 15.126 4.628 47 26 9.702 162 37 13 17.542 594 154 59 15.845 368 49 25 9.502 223 48 22 7.916 549 44 33 31.654 2.460 45 29 11.524 225 37 30 11.347 1.525 150 117 10.107 4.339 27 17 9.874 867 35 25 10.695 564 100 47 36.583 4.180 63 47 7.400 126 398 230 9.770 530 127 69 23.344 552 88 32 15.021 307 797 4.600 996.103 80.821 212 122 542.950 50.505 147 105 28.210 2.018 206 113 22.105 1.798 109 67 2.068 65 386 270 37.381 2.617 219 126 8.282 116 86 43 8.886 129 534 254 32.555 2.614 204 144 34.137 1.863 133 112 6.459 114 676 412 8.731 1.567 303 179 10.861 361 95 75 8.865 162 226 119 11.953 401 124 101 17.261 394 96 71 11.916 1.414 67 30 17.533 849 7 3 14.821 520 122 72 12.993 277 34 22 19.647 4.783 26 24 4.340 50 99 76 28.309 3.263 118 98 10.912 523 25 6 14.647 359 34 20 34.897 4.731 45 23 24.829 5.617 39 23 21.432 496 37 21 10.900 263 55 36 4.262 691 43 29 9.538 3.830 48 35 4.192 563 59 40 15.073 2.956 44 30 13.599 1.557 57 29 10.641 3.041 17 3 8.593 228 102 62 12.122 275 31 29 453.153 30.316 47 30 27.809 596 144 96 9.666 146 72 37 5.892 76 69 40 8.934 635 32 27 9.215 252 22 13 11.489 284 39 24 7.556 135 13 11 22.209 839 23 20 5.785 121 52 39 13.913 246 39 26 19.402 628 27 27 6.357 189 48 23 9.478 181 117 74 8.742 665 40 27 11.853 790 30 14 7.610 133 28 16 17.266 1.671 42 15 15.000 367 47 24 81.852 13.611 34 14 13.502 270 99 73 10.692 351 26 12 15.176 348 45 25 15.809 5.356 80 40 31.380 1.172 23 10 13.991 376 37 18 8.053 142 31 16 6.991 84 41 27 22.115 438 17 14 10.238 153 74 36 5.178 61 68 29 1.126.354 33.012 569 255 268.933 7.380 52 29 14.910 154 39 15 18.254 653 55 36 112.918 3.823 49 28 10.655 184 145 95 13.467 157 62 25 22.180 272 43 21 19.540 345 31 10 21.949 231 97 55 2.767 29 35 26 32.293 1.532 19 12 49.237 438 15 15 16.089 186 130 89 9.509 78 38 26 8.357 66 48 18 11.994 74 40 20 3.288 34 71 40 102.673 3.155 49 27 33.930 1.032 19 7 819 131 28 20 19.574 386 50 33 17.327 807 20 12 12.070 122 32 24 7.564 561 119 86 7.754 75 29 21 3.635 41 68 62 271.055 10.057 94 53 14.137 97 25 22 9.433 170 87 52 11.091 171 135 67 25.989 588 17 18 70.616 4.295 13 7 12.479 423 49 37 5.519 70 37 21 30.037 2.493 140 71 35.491 815 16 20 30.476 499 38 28 23.779 334 23 16 2.008 102 63 37 145.374 5.467 75 45 15.782 420 474 360 11.556 171 43 35 13.693 146 52 26 18.245 391 97 54 65.181 3.883 102 54 8.885 87 89 55 12.032 369 8 7 142.150 2.874 116 87 9.828 129 60 28 10.405 236 44 21 26.630 481 36 21 9.426 104 53 31 8.390 111 17 1 10.707 92 149 86 55.804 1.628 10 6 10.960 93 89 68 88.714 1.560 57 47 8.171 83 51 33 10.872 187 40 21 7.459 100 28 16 6.520 165 10 8 5.077 100 45 19 19.177 470 35 19 8.999 153 41 33 13.522 166 109 72 8.917 136 299 217 58.218 2.081 44 31 4.834 86 18 10 9.822 792 138 91 21.123 709 152 87 10.985 282 142 73 11.454 212 94 57 1.373.329 58.997 9 4 266.003 7.541 86 43 76.544 3.499 42 32 17.779 706 55 39 31.075 958 48 48 17.413 255 297 239 16.934 204 42 24 17.470 159 40 23 35.597 1.078 40 23 9.616 95 30 25 24.563 319 126 75 19.012 268 35 25 188.201 4.320 44 19 14.400 169 36 28 13.870 211 253 127 43.146 949 36 35 23.266 634 18 17 12.009 126 47 25 17.598 391 26 18 10.147 268 38 22 22.990 561 28 15 11.087 155 69 51 19.688 856 44 30 79.276 2.645 58 31 13.460 346 37 27 19.467 576 24 14 6.066 117 34 24 22.275 516 66 62 6.373 231 48 28 11.635 859 50 25 495.517 31.731 355 210 19.131 370 81 36 28.598 700 106 81 10.071 156 64 39 17.209 462 70 36 32.581 531 68 38 12.291 159 137 88 216.835 26.531 29 19 8.025 139 76 71 21.064 322 74 47 9.232 196 57 38 10.535 239 40 28 22.640 477 181 130 5.855 157 85 73 11.044 160 49 22 11.667 118 84 52 13.218 116 46 31 8.070 256 100 58 7.786 109 40 37 6.845 223 86 56 8.084 88 57 33 14.736 222 86 67 117.068 2.927 21 14 8.063 82 75 59 28.969 733 30 11 23.619 1.527 64 34 7.054 76 85 44 13.978 131 110 79 6.346 90 35 18 6.207 57 47 47 2.028 21 157 75 6.476 58 50 23 6.364 54 1.105 664 7.964 98 22 19 227.264 9.833 86 35 45.198 1.101 29 20 15.438 382 38 39 37.035 2.139 79 57 17.932 569 24 21 67.988 3.818 34 23 16.691 659 55 20 26.982 1.165 36 37 762.379 76.126 39 18 384.586 23.784 31 16 7.316 469 30 16 40.699 2.059 40 26 17.624 584 57 30 56.323 8.434 31 11 7.923 198 139 63 8.895 241 104 68 69.358 3.709 28 14 11.939 280 44 26 14.089 818 23 16 13.938 355 17 8 6.614 118 6 5 9.489 564 20 14 37.672 1.637 24 15 17.292 393 27 14 19.817 753 181 100 6.253 756 65 35 9.842 393 155 86 29.503 2.023 73 39 203.347 29.388 338 217 11.066 1.570 77 35 14.285 888 110 62
Names of X columns:
belgen buitenlanders huwelijken echtscheidingen
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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