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Data X:
27.72 41837160 91.51 2747.48 0.016 62.7 0.16 26.90 35204750 91.09 2760.01 0.016 62.7 0.17 25.86 42367740 93.00 2778.11 0.016 62.7 0.17 26.81 61427940 93.08 2844.72 0.016 62.7 0.16 26.31 26132090 94.13 2831.02 0.016 62.7 0.16 27.10 3799718 96.26 2858.42 0.016 62.7 0.17 27.00 28202230 94.29 2809.73 0.016 62.7 0.17 27.40 15809640 94.46 2843.07 0.016 62.7 0.16 27.27 17110160 95.53 2818.61 0.016 62.7 0.17 28.29 16835510 98.29 2836.33 0.016 62.7 0.17 30.01 43517670 102.01 2872.80 0.016 62.7 0.18 31.41 42958450 105.16 2895.33 0.016 62.7 0.17 31.91 30826830 105.34 2929.76 0.016 62.7 0.17 31.60 15549740 105.27 2930.45 0.016 62.7 0.16 31.84 21843070 102.19 2859.09 0.016 62.7 0.17 33.05 73424890 106.85 2892.42 0.016 62.7 0.17 32.06 24330740 103.05 2836.16 0.016 62.7 0.17 33.10 24785970 106.42 2854.06 0.016 62.7 0.16 32.23 28553940 105.17 2875.32 0.016 62.7 0.15 31.36 17659080 102.74 2849.49 0.016 62.7 0.15 31.09 19508980 106.27 2935.05 0.016 62.7 0.09 30.77 14110230 107.63 2951.23 0.0141 65.4 0.18 31.20 8765498 108.54 2976.08 0.0141 65.4 0.17 31.47 10027250 108.24 2976.12 0.0141 65.4 0.17 31.73 10943350 108.86 2937.33 0.0141 65.4 0.17 32.17 17755740 102.98 2931.77 0.0141 65.4 0.17 31.47 14238190 99.53 2902.33 0.0141 65.4 0.17 30.97 12997760 101.08 2887.98 0.0141 65.4 0.17 30.81 11299240 104.64 2866.19 0.0141 65.4 0.18 30.72 8102653 105.59 2908.47 0.0141 65.4 0.19 28.24 24549800 103.21 2896.94 0.0141 65.4 0.18 28.09 30410530 103.84 2910.04 0.0141 65.4 0.17 29.11 16807730 104.61 2942.60 0.0141 65.4 0.16 29.00 13671200 108.65 2965.90 0.0141 65.4 0.13 28.76 11854290 106.26 2925.30 0.0141 65.4 0.13 28.75 12383610 104.20 2890.15 0.0141 65.4 0.14 28.45 11512350 102.99 2862.99 0.0141 65.4 0.15 29.34 16749990 102.19 2854.24 0.0141 65.4 0.15 26.84 61009290 100.82 2893.25 0.0141 65.4 0.14 23.70 123011300 103.42 2958.09 0.0141 65.4 0.14 23.15 29253590 104.18 2945.84 0.0141 65.4 0.14 21.71 55998620 102.65 2939.52 0.0141 65.4 0.13 20.88 44488370 95.64 2920.21 0.0169 61.3 0.14 20.04 56264460 93.51 2909.77 0.0169 61.3 0.14 21.09 80626220 108.51 2967.90 0.0169 61.3 0.14 21.92 27733830 111.55 2989.91 0.0169 61.3 0.14 20.72 36699380 106.70 3015.86 0.0169 61.3 0.13 20.72 29514550 104.93 3011.25 0.0169 61.3 0.13 21.01 15605960 105.23 3018.64 0.0169 61.3 0.13 21.80 25714310 104.92 3020.86 0.0169 61.3 0.13 21.60 24904700 104.60 3022.52 0.0169 61.3 0.13 20.38 38971320 101.76 3016.98 0.0169 61.3 0.13 21.20 47682050 102.23 3030.93 0.0169 61.3 0.13 19.87 157188200 103.99 3062.39 0.0169 61.3 0.13 19.05 129057400 101.36 3076.59 0.0169 61.3 0.13 20.01 100818300 102.92 3076.21 0.0169 61.3 0.13 19.15 70483330 105.25 3067.26 0.0169 61.3 0.13 19.43 49779450 105.71 3073.67 0.0169 61.3 0.13 19.44 32747000 105.42 3053.40 0.0169 61.3 0.13 19.40 29588690 105.11 3069.79 0.0169 61.3 0.13 19.15 20663220 104.67 3073.19 0.0169 61.3 0.13 19.34 25402980 107.51 3077.14 0.0169 61.3 0.13 19.10 16071190 109.00 3081.19 0.0169 61.3 0.13 19.08 30571430 107.37 3048.71 0.0169 61.3 0.14 18.05 58612440 107.30 3066.96 0.0169 61.3 0.13 17.72 46177000 107.37 3075.06 0.0199 70.3 0.14 18.58 60657900 113.28 3069.27 0.0199 70.3 0.16 18.96 46028860 119.10 3135.81 0.0199 70.3 0.16 18.98 36325880 119.04 3136.42 0.0199 70.3 0.15 18.81 24752340 117.80 3104.02 0.0199 70.3 0.15 19.43 47343020 117.90 3104.53 0.0199 70.3 0.15 20.93 121399400 119.55 3114.31 0.0199 70.3 0.15 20.71 64896660 119.47 3155.83 0.0199 70.3 0.15 22.00 72707430 123.23 3183.95 0.0199 70.3 0.16 21.52 50593510 121.40 3178.67 0.0199 70.3 0.16 21.87 36696330 121.43 3177.80 0.0199 70.3 0.16 23.29 78525460 122.51 3182.62 0.0199 70.3 0.15 22.59 57115160 122.78 3175.96 0.0199 70.3 0.16 22.86 51163120 122.84 3179.96 0.0199 70.3 0.15 20.79 78968380 122.70 3160.78 0.0199 70.3 0.16 20.28 46169460 119.89 3117.73 0.0199 70.3 0.15 20.62 38212360 118.00 3093.70 0.0199 70.3 0.16 20.32 30061050 119.61 3136.60 0.0199 70.3 0.14 21.66 65415370 120.40 3116.23 0.0199 70.3 0.09 21.99 51198150 117.94 3113.53 0.0216 73.1 0.15 22.27 29276680 118.77 3120.04 0.0216 73.1 0.16 21.83 31940720 121.68 3135.23 0.0216 73.1 0.16 21.94 46549400 121.98 3149.46 0.0216 73.1 0.15 20.91 40483780 118.83 3136.19 0.0216 73.1 0.15 20.40 32190200 117.97 3112.35 0.0216 73.1 0.15 20.22 27125670 113.07 3065.02 0.0216 73.1 0.16 19.64 39282420 111.98 3051.78 0.0216 73.1 0.16 19.75 21803710 113.77 3049.41 0.0216 73.1 0.16 19.51 18743920 110.41 3044.11 0.0216 73.1 0.16 19.52 20154860 110.85 3064.18 0.0216 73.1 0.16 19.48 21816100 111.18 3101.17 0.0216 73.1 0.16 19.88 44020450 109.42 3104.12 0.0216 73.1 0.15 18.97 52059860 108.87 3072.87 0.0216 73.1 0.15 19.00 34769600 106.72 3005.62 0.0216 73.1 0.16 19.32 32269470 107.28 3016.96 0.0216 73.1 0.15 19.50 72281000 104.13 2990.46 0.0216 73.1 0.15 23.22 228364700 107.55 2981.70 0.0216 73.1 0.17 22.56 76050080 105.72 2986.12 0.0216 73.1 0.16 21.94 9999999 104.55 2987.95 0.0216 73.1 0.16 21.11 99311480 106.93 2977.23 0.0216 73.1 0.18 21.21 37631000 106.85 3020.06 0.0176 73.1 0.17 21.18 38308550 106.78 2982.13 0.0176 73.1 0.16 21.25 31752420 107.29 2999.66 0.0176 73.1 0.17 21.17 29030780 104.14 3011.93 0.0176 73.1 0.16 20.47 33352920 101.21 2937.29 0.0176 73.1 0.16 19.99 34106840 96.35 2895.58 0.0176 73.1 0.16 19.21 42257790 95.62 2904.87 0.0176 73.1 0.16 20.07 67220540 99.00 2904.26 0.0176 73.1 0.16 19.86 71524510 99.26 2883.89 0.0176 73.1 0.16 22.36 229081600 98.77 2846.81 0.0176 73.1 0.16 22.17 78808770 100.65 2836.94 0.0176 73.1 0.16 23.56 107091400 103.13 2853.13 0.0176 73.1 0.16 22.92 84944370 105.53 2916.07 0.0176 73.1 0.16 23.10 46515660 106.76 2916.68 0.0176 73.1 0.16 24.32 89720920 107.59 2926.55 0.0176 73.1 0.16 23.99 29520310 107.62 2966.85 0.0176 73.1 0.16 25.94 123513900 108.82 2976.78 0.0176 73.1 0.16 26.15 85687430 107.59 2967.79 0.0176 73.1 0.16 26.36 49113040 107.85 2991.78 0.0176 73.1 0.16 27.32 88572990 107.11 3012.03 0.0176 73.1 0.16 28.00 126867400 108.14 3010.24 0.0176 73.1 0.16
Names of X columns:
FACEBOOK VOLUME LINKEDIN NASDAQ INF.CONS.CONF FED FUNDS.RATE
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
0 seconds
R Server
Big Analytics Cloud Computing Center
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