Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
210907 79 112285 120982 58 84786 176508 60 83123 179321 108 101193 123185 49 38361 52746 0 68504 385534 121 119182 33170 1 22807 101645 20 17140 149061 43 116174 165446 69 57635 237213 78 66198 173326 86 71701 133131 44 57793 258873 104 80444 180083 63 53855 324799 158 97668 230964 102 133824 236785 77 101481 135473 82 99645 202925 115 114789 215147 101 99052 344297 80 67654 153935 50 65553 132943 83 97500 174724 123 69112 174415 73 82753 225548 81 85323 223632 105 72654 124817 47 30727 221698 105 77873 210767 94 117478 170266 44 74007 260561 114 90183 84853 38 61542 294424 107 101494 101011 30 27570 215641 71 55813 325107 84 79215 7176 0 1423 167542 59 55461 106408 33 31081 96560 42 22996 265769 96 83122 269651 106 70106 149112 56 60578 175824 57 39992 152871 59 79892 111665 39 49810 116408 34 71570 362301 76 100708 78800 20 33032 183167 91 82875 277965 115 139077 150629 85 71595 168809 76 72260 24188 8 5950 329267 79 115762 65029 21 32551 101097 30 31701 218946 76 80670 244052 101 143558 341570 94 117105 103597 27 23789 233328 92 120733 256462 123 105195 206161 75 73107 311473 128 132068 235800 105 149193 177939 55 46821 207176 56 87011 196553 41 95260 174184 72 55183 143246 67 106671 187559 75 73511 187681 114 92945 119016 118 78664 182192 77 70054 73566 22 22618 194979 66 74011 167488 69 83737 143756 105 69094 275541 116 93133 243199 88 95536 182999 73 225920 135649 99 62133 152299 62 61370 120221 53 43836 346485 118 106117 145790 30 38692 193339 100 84651 80953 49 56622 122774 24 15986 130585 67 95364 112611 46 26706 286468 57 89691 241066 75 67267 148446 135 126846 204713 68 41140 182079 124 102860 140344 33 51715 220516 98 55801 243060 58 111813 162765 68 120293 182613 81 138599 232138 131 161647 265318 110 115929 85574 37 24266 310839 130 162901 225060 93 109825 232317 118 129838 144966 39 37510 43287 13 43750 155754 74 40652 164709 81 87771 201940 109 85872 235454 151 89275 220801 51 44418 99466 28 192565 92661 40 35232 133328 56 40909 61361 27 13294 125930 37 32387 100750 83 140867 224549 54 120662 82316 27 21233 102010 28 44332 101523 59 61056 243511 133 101338 22938 12 1168 41566 0 13497 152474 106 65567 61857 23 25162 99923 44 32334 132487 71 40735 317394 116 91413 21054 4 855 209641 62 97068 22648 12 44339 31414 18 14116 46698 14 10288 131698 60 65622 91735 7 16563 244749 98 76643 184510 64 110681 79863 29 29011 128423 32 92696 97839 25 94785 38214 16 8773 151101 48 83209 272458 100 93815 172494 46 86687 108043 45 34553 328107 129 105547 250579 130 103487 351067 136 213688 158015 59 71220 98866 25 23517 85439 32 56926 229242 63 91721 351619 95 115168 84207 14 111194 120445 36 51009 324598 113 135777 131069 47 51513 204271 92 74163 165543 70 51633 141722 19 75345 116048 50 33416 250047 41 83305 299775 91 98952 195838 111 102372 173260 41 37238 254488 120 103772 104389 135 123969 136084 27 27142 199476 87 135400 92499 25 21399 224330 131 130115 135781 45 24874 74408 29 34988 81240 58 45549 14688 4 6023 181633 47 64466 271856 109 54990 7199 7 1644 46660 12 6179 17547 0 3926 133368 37 32755 95227 37 34777 152601 46 73224 98146 15 27114 79619 42 20760 59194 7 37636 139942 54 65461 118612 54 30080 72880 14 24094 65475 16 69008 99643 33 54968 71965 32 46090 77272 21 27507 49289 15 10672 135131 38 34029 108446 22 46300 89746 28 24760 44296 10 18779 77648 31 21280 181528 32 40662 134019 32 28987 124064 43 22827 92630 27 18513 121848 37 30594 52915 20 24006 81872 32 27913 58981 0 42744 53515 5 12934 60812 26 22574 56375 10 41385 65490 27 18653 80949 11 18472 76302 29 30976 104011 25 63339 98104 55 25568 67989 23 33747 30989 5 4154 135458 43 19474 73504 23 35130 63123 34 39067 61254 36 13310 74914 35 65892 31774 0 4143 81437 37 28579 87186 28 51776 50090 16 21152 65745 26 38084 56653 38 27717 158399 23 32928 46455 22 11342 73624 30 19499 38395 16 16380 91899 18 36874 139526 28 48259 52164 32 16734 51567 21 28207 70551 23 30143 84856 29 41369 102538 50 45833 86678 12 29156 85709 21 35944 34662 18 36278 150580 27 45588 99611 41 45097 19349 13 3895 99373 12 28394 86230 21 18632 30837 8 2325 31706 26 25139 89806 27 27975 62088 13 14483 40151 16 13127 27634 2 5839 76990 42 24069 37460 5 3738 54157 37 18625 49862 17 36341 84337 38 24548 64175 37 21792 59382 29 26263 119308 32 23686 76702 35 49303 103425 17 25659 70344 20 28904 43410 7 2781 104838 46 29236 62215 24 19546 69304 40 22818 53117 3 32689 19764 10 5752 86680 37 22197 84105 17 20055 77945 28 25272 89113 19 82206 91005 29 32073 40248 8 5444 64187 10 20154 50857 15 36944 56613 15 8019 62792 28 30884 72535 17 19540
Names of X columns:
time blogged size
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
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation