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Data X:
321.61 26.75 0 0 1546.66 0.037987 345.85 22.33 0 0 1570.98 0.038863 338.60 16.38 0 0 1709.05 0.031132 345.64 12.77 0 0 1818.6 0.022556 340.71 11.89 0 0 1783.97 0.015903 342.49 13.49 0 0 1876.7 0.014911 342.65 11.95 0 0 1892.71 0.017658 348.68 9.88 0 0 1775.3 0.01577 377.36 13.42 0 0 1898.33 0.015741 418.05 14.03 0 0 1767.57 0.017544 423.13 14.01 0 0 1877.8 0.014719 397.69 14.47 0 0 1914.22 0.012844 390.80 15.44 0 0 1895.94 0.010979 408.29 18.1 0 0 2158.03 0.014599 401.02 17.28 0 0 2223.98 0.021043 409.24 17.74 0 0 2304.68 0.030331 439.28 18.05 0 0 2286.35 0.037753 459.95 18.41 0 0 2291.56 0.038567 449.66 18.71 0 0 2418.52 0.03653 451.14 19.62 0 0 2572.06 0.039269 460.66 18.88 0 0 2662.94 0.042844 460.23 18.32 0 0 2596.27 0.043557 465.69 18.63 0 0 1993.52 0.045331 468.01 17.87 0 0 1833.54 0.04529 486.74 16.77 0 0 1938.82 0.044344 475.89 16.5 0 0 1958.21 0.040468 441.52 15.9 0 0 2071.61 0.039427 443.63 14.86 0 0 1988.05 0.039251 451.62 16.42 0 0 2032.32 0.039042 451.14 16.36 0 0 2031.11 0.038904 450.88 15.49 0 0 2141.7 0.039648 437.56 14.47 0 0 2128.72 0.041301 431.18 14.57 0 0 2031.64 0.04021 412.02 13.22 0 0 2112.9 0.041739 407.14 12.23 0 0 2148.64 0.042498 420.48 12.53 0 0 2114.5 0.042461 418.92 14.68 0 0 2168.56 0.044194 403.57 16.45 0 0 2342.31 0.046672 387.55 16.52 0 0 2258.38 0.048276 390.02 18.1 0 0 2293.61 0.049785 384.37 19.39 0 0 2418.79 0.051238 370.62 18.22 0 0 2480.14 0.053617 367.90 17.8 0 0 2440.05 0.051695 374.91 17.67 0 0 2660.65 0.049789 365.15 16.87 0 0 2737.26 0.047059 361.91 17.69 0 0 2692.81 0.043406 367.03 18.41 0 0 2645.07 0.044925 395.19 18.38 0 0 2706.26 0.04655 409.25 19.37 0 0 2753.19 0.046473 410.49 20.59 0 0 2590.53 0.052023 416.58 19.68 0 0 2627.24 0.052632 392.21 18.12 0 0 2707.2 0.05233 374.29 16.32 0 0 2656.75 0.047116 369.05 16.21 0 0 2876.65 0.043619 352.19 14.93 0 0 2880.68 0.046737 362.85 16.81 0 0 2905.19 0.048232 395.47 26.54 0 0 2614.35 0.05618 388.82 33.62 0 0 2452.47 0.0616 380.39 34.85 0 0 2442.32 0.062898 381.73 31.54 0 0 2559.64 0.062748 377.69 26.61 0 0 2633.65 0.061063 383.04 22.81 0 0 2736.38 0.056515 363.89 18.53 0 0 2882.17 0.053125 363.23 18.21 0 0 2913.85 0.048951 358.37 18.49 0 0 2887.86 0.048875 356.97 18.72 0 0 3027.49 0.049536 366.87 17.78 0 0 2906.74 0.046959 367.57 19.02 0 0 3024.81 0.044479 355.88 19.3 0 0 3043.59 0.037994 348.88 19.95 0 0 3016.76 0.033911 358.77 21.56 0 0 3069.9 0.029213 360.42 20.41 0 0 2894.67 0.029895 361.08 17.63 0 0 3168.82 0.030643 354.57 17.52 0 0 3223.38 0.026003 353.73 17.65 0 0 3267.66 0.02819 344.20 17.35 0 0 3235.46 0.031852 338.34 18.65 0 0 3359.11 0.031805 337.21 19.52 0 0 3396.87 0.030236 340.96 20.88 0 0 3318.51 0.030882 353.29 20.18 0 0 3393.77 0.031571 342.67 19.62 0 0 3257.34 0.031479 345.71 20.19 0 0 3271.65 0.029883 344.17 20.04 0 0 3226.27 0.032023 334.92 18.9 0 0 3305.15 0.030479 334.81 17.93 0 0 3301.1 0.029007 329.05 17.24 0 0 3310.02 0.032585 329.31 18.23 0 0 3370.8 0.032468 330.25 18.5 0 0 3435.1 0.030869 341.89 18.44 0 0 3427.54 0.032258 367.74 18.17 0 0 3527.42 0.032212 371.93 17.37 0 0 3516.07 0.029957 392.79 16.37 0 0 3539.46 0.027758 377.97 16.43 0 0 3651.24 0.027679 354.93 15.8 0 0 3555.11 0.026893 364.40 16.44 0 0 3680.58 0.027504 374.05 15.09 0 0 3683.94 0.026761 383.63 13.36 0 0 3754.08 0.027484 386.56 14.17 0 0 3978.35 0.025245 381.90 13.75 0 0 3832.01 0.025157 384.08 13.69 0 0 3635.95 0.02507 377.29 15.15 0 0 3681.68 0.023611 381.54 16.43 0 0 3758.36 0.022885 385.60 17.23 0 0 3624.95 0.024931 385.47 18.04 0 0 3764.49 0.027701 380.40 16.98 0 0 3913.41 0.029006 391.74 16.13 0 0 3843.18 0.029635 389.57 16.48 0 0 3908.11 0.026081 384.29 17.2 0 0 3739.22 0.026749 379.26 16.13 0 0 3834.43 0.026749 378.44 16.88 0 0 3843.85 0.028044 376.63 17.44 0 0 4011.04 0.02863 382.48 17.35 0 0 4157.68 0.028533 390.89 18.77 0 0 4321.26 0.030529 385.04 18.43 0 0 4465.13 0.031864 387.58 17.33 0 0 4556.9 0.030405 386.19 16.06 0 0 4708.46 0.027628 383.78 16.49 0 0 4610.55 0.026174 383.10 16.77 0 0 4789.07 0.025435 383.25 16.18 0 0 4755.47 0.028094 385.19 16.82 0 0 5074.48 0.026052 387.35 17.93 0 0 5117.11 0.025384 400.49 17.79 0 0 5395.29 0.027279 404.53 17.69 0 0 5485.61 0.026508 396.15 19.46 0 0 5587.13 0.028402 392.79 20.78 0 0 5569.07 0.028966 391.96 19.12 0 0 5643.17 0.028909 385.04 18.56 0 0 5654.62 0.027541 383.58 19.56 0 0 5528.9 0.029508 387.46 20.19 0 0 5616.2 0.028777 382.90 22.14 0 0 5882.16 0.030026 381.04 23.43 0 0 6029.37 0.029928 377.69 22.25 0 0 6521.69 0.032552 368.95 23.51 0 0 6448.26 0.033225 353.87 23.29 0 0 6813.08 0.03044 347.03 20.54 0 0 6877.73 0.030342 351.49 19.42 0 0 6583.47 0.027617 344.23 17.98 0 0 7008.98 0.024952 344.09 19.47 0 0 7331.03 0.02235 340.51 18.02 0 0 7672.78 0.022974 323.90 18.45 0 0 8222.6 0.022293 324.02 18.79 0 0 7622.41 0.02225 323.11 18.73 0 0 7945.25 0.021546 324.36 20.12 0 0 7442.07 0.020846 305.55 19.16 0 0 7823.12 0.018285 288.59 17.24 0 0 7908.24 0.017024 289.15 15.07 0 0 7906.49 0.015713 297.49 14.18 0 0 8545.71 0.014411 295.94 13.24 0 0 8799.8 0.01375 308.29 13.39 0 0 9063.36 0.014357 299.10 13.97 0 0 8899.94 0.016864 292.32 12.48 0 0 8952.01 0.016843 292.87 12.72 0 0 8883.28 0.016822 284.11 12.49 0 0 7539.06 0.016169 288.98 13.8 0 0 7842.61 0.014888 295.93 13.26 0 0 8592.9 0.014851 294.17 11.88 0 0 9116.54 0.01548 291.68 10.41 0 0 9181.42 0.016119 287.07 11.32 0 0 9358.82 0.016708 287.33 10.75 0 0 9306.57 0.016059 285.96 12.86 0 0 9786.15 0.017263 282.62 15.73 0 0 10789.03 0.022769 276.44 16.12 0 0 10559.73 0.020885 261.31 16.24 0 0 10970.79 0.019632 256.08 18.75 0 0 10655.14 0.021446 256.69 20.21 0 0 10829.27 0.022644 264.74 22.37 0 0 10336.94 0.026284 310.72 22.19 0 0 10729.85 0.02561 293.18 24.22 0 0 10877.8 0.02622 283.07 25.01 0 0 11497.11 0.026846 284.32 25.21 0 0 10940.52 0.027389 299.86 27.15 0 0 10128.3 0.032219 286.39 27.49 0 0 10921.91 0.037576 279.69 23.45 0 0 10733.9 0.030686 275.19 27.23 0 0 10522.32 0.031889 285.73 29.62 0 0 10447.88 0.037304 281.59 28.16 0 0 10521.97 0.036593 274.47 29.41 0 0 11215.9 0.034111 273.68 32.08 0 0 10650.91 0.034544 270.00 31.4 0 0 10971.13 0.034483 266.01 32.33 0 0 10414.48 0.034462 271.45 25.28 0 0 10786.84 0.033868 265.49 25.95 0 0 10887.35 0.037322 261.87 27.24 0 0 10495.27 0.035336 263.03 25.02 0 0 9878.77 0.029206 260.48 25.66 0 0 10734.96 0.032691 272.36 27.55 0 0 10911.93 0.036152 269.82 26.97 0 0 10502.39 0.032483 267.53 24.8 0 0 10522.8 0.027199 272.39 25.81 0 0 9949.74 0.027199 283.42 25.03 0 0 8847.55 0.026482 283.06 20.73 0 0 9075.13 0.021264 276.16 18.69 0 0 9851.55 0.018955 275.85 18.52 0 0 10021.49 0.015517 281.51 19.15 0 0 9920.01 0.011422 295.50 19.98 0 0 10106.12 0.011377 294.06 23.64 0 0 10403.94 0.014756 302.68 25.43 0 0 9946.22 0.016393 314.58 25.69 0 0 9925.25 0.011818 321.18 24.49 0 0 9243.26 0.010674 313.29 25.75 0 0 8736.59 0.014648 310.25 26.78 0 0 8663.5 0.018028 319.14 28.28 0 0 7591.93 0.015143 316.56 27.53 0 0 8397.03 0.020259 319.07 24.79 0 0 8896.09 0.021984 331.92 27.89 0 0 8341.63 0.023769 356.86 30.77 0 0 8053.81 0.025974 358.97 32.88 0 0 7891.08 0.029809 340.55 30.36 1 0 7992.13 0.030201 328.18 25.49 1 0 8480.09 0.022247 355.68 26.06 1 0 8850.26 0.020578 356.35 27.91 1 0 8985.44 0.021123 350.99 28.59 1 0 9233.8 0.021099 359.77 29.68 1 0 9415.82 0.021583 378.95 26.88 1 0 9275.06 0.023204 378.92 29.01 1 0 9801.12 0.020408 389.91 29.12 1 0 9782.46 0.01765 406.11 29.95 1 0 10453.92 0.018795 413.79 31.4 1 0 10488.07 0.019263 404.95 31.32 1 0 10583.92 0.016931 406.67 33.67 1 0 10357.7 0.017372 403.26 33.71 1 0 10225.57 0.022851 383.78 37.63 1 0 10188.45 0.030518 392.48 35.54 1 0 10435.48 0.032662 398.09 37.93 1 0 10139.71 0.029908 400.51 42.08 1 0 10173.92 0.026544 405.28 41.65 1 0 10080.27 0.025378 420.46 46.87 1 0 10027.47 0.031892 439.38 42.23 1 0 10428.02 0.03523 442.08 39.09 1 0 10783.01 0.032556 424.03 42.89 1 0 10489.94 0.029698 423.35 44.56 1 0 10766.23 0.030075 434.32 50.93 1 0 10503.76 0.031483 429.23 50.64 1 0 10192.51 0.035106 421.87 47.81 1 0 10467.48 0.028027 430.66 53.89 1 0 10274.97 0.025303 424.48 56.37 1 0 10640.91 0.031679 437.93 61.87 1 0 10481.6 0.036412 456.05 61.65 1 0 10568.7 0.046867 469.90 58.19 1 0 10440.07 0.043478 476.67 54.98 1 0 10805.87 0.034555 510.10 56.47 1 0 10717.5 0.034157 549.86 62.36 1 0 10864.86 0.039853 555.00 59.71 1 0 10993.41 0.035975 557.09 60.93 1 0 11109.32 0.033626 610.65 68 1 0 11367.14 0.035457 675.39 68.61 1 0 11168.31 0.041667 596.15 68.29 1 0 11150.22 0.043188 633.71 72.51 1 0 11185.68 0.041453 632.33 71.81 1 0 11381.15 0.038187 598.06 61.97 1 0 11679.07 0.020624 585.78 57.95 1 0 12080.73 0.013052 627.83 58.13 1 0 12221.93 0.019737 629.42 61 1 0 12463.15 0.025407 631.17 53.4 1 0 12621.69 0.020756 664.75 57.58 1 0 12268.63 0.024152 654.90 60.6 1 0 12354.35 0.027788 679.37 65.1 1 0 13062.92 0.025737 666.92 65.1 1 0 13627.64 0.026909 655.49 68.19 1 0 13408.62 0.02687 665.30 73.67 1 0 13211.99 0.023582 665.41 70.13 1 1 13357.74 0.019701 712.65 76.91 1 1 13895.63 0.027551 754.60 82.15 1 1 13930.01 0.035362 806.25 91.27 1 1 13371.72 0.043062 803.20 89.43 1 1 13264.82 0.040813 889.60 90.82 1 1 12650.36 0.042803 922.30 93.75 1 1 12266.39 0.040266 968.43 101.84 1 1 12262.89 0.039815 909.70 109.05 1 1 12820.13 0.039369 890.51 122.77 1 1 12638.32 0.041755 889.49 131.52 1 1 11350.01 0.050218 939.77 132.55 1 1 11378.02 0.056001 838.31 114.57 1 1 11543.55 0.053719 829.93 99.29 1 1 10850.66 0.049369 806.62 72.69 1 1 9325.01 0.036552 760.86 54.04 1 1 8829.04 0.010696 822.00 41.53 1 1 8776.39 0.000914 859.19 43.91 1 1 8000.86 0.000298 943.16 41.76 1 1 7062.93 0.002362 924.27 46.95 1 1 7608.92 -0.003836 889.50 50.28 1 1 8168.12 -0.007369 930.20 58.1 1 1 8500.33 -0.012814 945.67 69.13 1 1 8447 -0.014268 934.23 64.65 1 1 9171.61 -0.020972 949.67 71.63 1 1 9496.28 -0.014843 996.59 68.38 1 1 9712.28 -0.012862 1043.16 74.08 1 1 9712.73 -0.001828 1127.04 77.56 1 1 10344.84 0.018383 1126.22 74.88 1 1 10428.05 0.027213 1116.51 77.09 1 1 10067.33 0.026257 1095.41 74.7 1 1 10325.26 0.021433 1113.34 79.3 1 1 10856.63 0.02314 1148.69 84.19 1 1 11008.61 0.022364 1205.43 75.56 1 1 10136.63 0.02021 1232.92 74.73 1 1 9774.02 0.010533 1192.97 74.49 1 1 10465.94 0.012352 1215.81 75.93 1 1 10014.72 0.011481 1270.98 76.14 1 1 10788.05 0.011437 1342.02 81.72 1 1 11118.4 0.011722
Names of X columns:
Gold Oil ETF Crisis Dow Inflation
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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