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Data X:
81 3 79 30 115 94 112285 24188 146283 144 145 55 4 58 28 109 103 84786 18273 98364 103 101 50 12 60 38 146 93 83123 14130 86146 98 98 125 2 108 30 116 103 101193 32287 96933 135 132 40 1 49 22 68 51 38361 8654 79234 61 60 37 3 0 26 101 70 68504 9245 42551 39 38 63 0 121 25 96 91 119182 33251 195663 150 144 44 0 1 18 67 22 22807 1271 6853 5 5 88 0 20 11 44 38 17140 5279 21529 28 28 66 5 43 26 100 93 116174 27101 95757 84 84 57 0 69 25 93 60 57635 16373 85584 80 79 74 0 78 38 140 123 66198 19716 143983 130 127 49 7 86 44 166 148 71701 17753 75851 82 78 52 7 44 30 99 90 57793 9028 59238 60 60 88 3 104 40 139 124 80444 18653 93163 131 131 36 9 63 34 130 70 53855 8828 96037 84 84 108 0 158 47 181 168 97668 29498 151511 140 133 43 4 102 30 116 115 133824 27563 136368 151 150 75 3 77 31 116 71 101481 18293 112642 91 91 32 0 82 23 88 66 99645 22530 94728 138 132 44 7 115 36 139 134 114789 15977 105499 150 136 85 0 101 36 135 117 99052 35082 121527 124 124 86 1 80 30 108 108 67654 16116 127766 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27 8 30 24 18513 6782 44683 31 30 45 0 37 17 64 52 30594 5401 35619 41 41 54 0 20 18 68 49 24006 6521 21920 26 25 25 0 32 16 64 61 27913 10856 45608 23 23 38 7 0 23 91 61 42744 2154 7721 14 14 52 2 5 22 88 81 12934 6117 20634 16 16 67 0 26 13 52 43 22574 5238 29788 25 26 74 7 10 13 49 40 41385 4820 31931 21 21 38 3 27 16 62 40 18653 5615 37754 32 27 30 0 11 16 61 56 18472 4272 32505 9 9 26 0 29 20 76 68 30976 8702 40557 35 33 67 6 25 22 88 79 63339 15340 94238 42 42 132 2 55 17 66 47 25568 8030 44197 68 68 42 0 23 18 71 57 33747 9526 43228 32 32 35 0 5 17 68 41 4154 1278 4103 6 6 118 3 43 12 48 29 19474 4236 44144 68 67 68 0 23 7 25 3 35130 3023 32868 33 33 43 1 34 17 68 60 39067 7196 27640 84 77 76 1 36 14 41 30 13310 3394 14063 46 46 64 0 35 23 90 79 65892 6371 28990 30 30 48 1 0 17 66 47 4143 1574 4694 0 0 64 0 37 14 54 40 28579 9620 42648 36 36 56 0 28 15 59 48 51776 6978 64329 47 46 71 0 16 17 60 36 21152 4911 21928 20 18 75 0 26 21 77 42 38084 8645 25836 50 48 39 0 38 18 68 49 27717 8987 22779 30 29 42 0 23 18 72 57 32928 5544 40820 30 28 39 0 22 17 67 12 11342 3083 27530 34 34 93 0 30 17 64 40 19499 6909 32378 33 33 38 0 16 16 63 43 16380 3189 10824 34 34 60 0 18 15 59 33 36874 6745 39613 37 33 71 0 28 21 84 77 48259 16724 60865 83 80 52 0 32 16 64 43 16734 4850 19787 32 32 27 2 21 14 56 45 28207 7025 20107 30 30 59 0 23 15 54 47 30143 6047 36605 43 41 40 1 29 17 67 43 41369 7377 40961 41 41 79 1 50 15 58 45 45833 9078 48231 51 51 44 0 12 15 59 50 29156 4605 39725 19 18 65 0 21 10 40 35 35944 3238 21455 37 34 10 0 18 6 22 7 36278 8100 23430 33 31 124 0 27 22 83 71 45588 9653 62991 41 39 81 0 41 21 81 67 45097 8914 49363 54 54 15 0 13 1 2 0 3895 786 9604 14 14 92 1 12 18 72 62 28394 6700 24552 25 24 42 0 21 17 61 54 18632 5788 31493 25 24 10 0 8 4 15 4 2325 593 3439 8 8 24 0 26 10 32 25 25139 4506 19555 26 26 64 0 27 16 62 40 27975 6382 21228 20 19 45 1 13 16 58 38 14483 5621 23177 11 11 22 0 16 9 36 19 13127 3997 22094 14 14 56 0 2 16 59 17 5839 520 2342 3 1 94 0 42 17 68 67 24069 8891 38798 40 39 19 0 5 7 21 14 3738 999 3255 5 5 35 0 37 15 55 30 18625 7067 24261 38 37 32 0 17 14 54 54 36341 4639 18511 32 32 35 0 38 14 55 35 24548 5654 40798 41 38 48 0 37 18 72 59 21792 6928 28893 46 47 49 0 29 12 41 24 26263 1514 21425 47 47 48 0 32 16 61 58 23686 9238 50276 37 37 62 0 35 21 67 42 49303 8204 37643 51 51 96 1 17 19 76 46 25659 5926 30377 49 45 45 0 20 16 64 61 28904 5785 27126 21 21 63 0 7 1 3 3 2781 4 13 1 1 71 1 46 16 63 52 29236 5930 42097 44 42 26 0 24 10 40 25 19546 3710 24451 26 26 48 6 40 19 69 40 22818 705 14335 21 21 29 3 3 12 48 32 32689 443 5084 4 4 19 1 10 2 8 4 5752 2416 9927 10 10 45 2 37 14 52 49 22197 7747 43527 43 43 45 0 17 17 66 63 20055 5432 27184 34 34 67 0 28 19 76 67 25272 4913 21610 32 31 30 0 19 14 43 32 82206 2650 20484 20 19 36 3 29 11 39 23 32073 2370 20156 34 34 34 1 8 4 14 7 5444 775 6012 6 6 36 0 10 16 61 54 20154 5576 18475 12 11 34 0 15 20 71 37 36944 1352 12645 24 24 37 1 15 12 44 35 8019 3080 11017 16 16 46 0 28 15 60 51 30884 10205 37623 72 72 44 0 17 16 64 39 19540 6095 35873 27 21
Names of X columns:
compendium_views_pr shared_compendiums blogged_computations compendiums_reviewed feedback_messages_p1 feedback_messages_p120 totsize totrevisions totseconds tothyperlinks totblogs
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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