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Data X:
17.98 586.9642 404.45 2342.32 83036 160984 17.83 582.1727 387.97 2258.39 75000 147727 19.45 586.19396 390.27 2293.62 83036 165384 21.04 590.49531 384.72 2418.80 80357 151202 20.03 589.70506 371.35 2480.15 83036 135237 20.01 590.07518 367.73 2440.06 80357 143684 19.64 595.2568 375.21 2660.66 83036 150327 18.52 604.1796 365.55 2737.27 83036 164909 19.59 605.00987 361.80 2692.82 80357 154582 20.09 610.71165 366.80 2645.08 83036 162221 19.82 618.31404 394.36 2706.27 80357 165019 21.09 614.74292 409.66 2753.20 83036 169673 22.64 609.16284 410.12 2590.54 54475 150297 22.11 611.73756 416.54 2627.25 49203 146944 20.42 620.77222 393.66 2707.21 54475 156522 18.58 618.00755 374.93 2656.76 52718 148536 18.24 612.3328 368.85 2876.66 54475 126854 16.87 604.04309 352.66 2880.69 52718 99485 18.64 605.08732 361.82 2905.20 54475 135265 27.17 569.6086 394.86 2614.36 54475 157406 33.69 595.0899 389.56 2452.48 52718 153170 35.92 598.49214 381.33 2442.33 54475 147381 32.30 606.67306 381.87 2559.65 52718 136753 27.34 608.78857 378.16 2633.66 54475 158421 24.96 606.9776 384.59 2736.39 57215 173332 20.52 603.86769 363.75 2882.18 51678 121795 19.86 606.42144 363.39 2913.86 57215 123438 20.82 592.42386 358.05 2887.87 55370 103478 21.24 590.6524 357.12 3027.50 57215 115420 20.20 592.57978 366.36 2906.75 55370 120867 21.42 602.51677 368.01 3024.82 57215 120728 21.69 595.5471 356.72 3043.60 57215 155286 21.86 605.94461 348.46 3016.77 55370 172705 23.23 605.4981 358.83 3069.10 57215 156418 22.47 607.95824 359.96 2894.68 55370 148870 19.52 612.04311 361.88 3168.83 57215 171422 18.82 612.7243 354.44 3223.39 59811 173574 19.00 604.3511 353.85 3267.67 55952 134051 18.92 597.85685 344.64 3235.47 59811 123753 20.24 601.4573 338.73 3359.12 57881 117862 20.94 590.17392 337.04 3396.88 59811 94628 22.38 592.59948 340.78 3318.52 57881 133387 21.76 597.24933 352.45 3393.78 59811 147477 21.35 597.11685 343.60 3257.35 59811 165077 21.90 599.7328 345.30 3271.66 57881 150981 21.69 607.71194 344.28 3226.28 59811 167026 20.34 604.82143 334.92 3305.16 57881 169155 19.41 608.02913 334.66 3301.11 59811 160271 19.08 606.1822 328.99 3310.03 52529 170293 20.05 609.73516 329.31 3370.81 47446 151312 20.35 602.5858 329.97 3435.11 52529 160860 20.27 595.55291 341.95 3427.55 50835 137556 19.94 597.41659 367.04 3527.43 52529 106249 19.07 594.57492 371.91 3516.08 50835 108464 17.87 600.63662 392.03 3539.47 52529 121223 18.01 598.85984 379.80 3651.25 52529 169114 17.51 598.97301 355.56 3555.12 50835 155352 18.15 603.61033 364.00 3680.59 52529 159722 16.70 604.24871 373.94 3683.95 50835 166638 14.51 608.12389 383.24 3754.09 52529 165920 15.00 610.69475 387.11 3978.36 50086 212488 14.78 608.90087 381.66 3832.02 45239 161262 14.66 607.98959 384.00 3635.96 50086 168545 16.38 603.37721 377.91 3681.69 48471 150959 17.88 607.81136 381.34 3758.37 50086 114509 19.07 610.96411 385.71 3624.96 48471 128341 19.65 606.97026 385.45 3764.50 50086 180244 18.38 605.89335 380.21 3913.42 50086 159268 17.46 611.97502 391.35 3843.19 48471 149027 17.71 616.95077 390.16 3908.12 50086 173723 18.10 618.69499 384.38 3739.23 48471 166053 17.16 621.90975 379.48 3834.44 50086 162952 17.99 617.85264 378.74 3843.86 50223 151625 18.53 623.04912 376.75 4011.05 45363 126219 18.55 615.57 381.82 4157.69 50223 152071 19.87 623.58574 391.34 4321.27 48603 171245 19.74 623.63317 385.23 4465.14 50223 144467 18.42 615.04568 387.62 4556.10 48603 137722 17.30 624.57004 386.14 4708.47 50223 198451 18.03 625.8824 383.50 4610.56 50223 203411 18.23 629.83163 382.93 4789.08 48603 174618 17.44 626.42181 383.20 4755.48 50223 192387 17.99 628.40528 385.21 5074.49 48603 171786 19.04 632.30782 387.44 5117.12 50223 167966 18.88 631.60361 398.70 5395.30 51874 181481 19.07 635.57585 404.92 5485.62 48527 141108 21.36 634.08687 396.51 5587.14 51874 164991 23.57 632.61106 392.87 5569.08 50200 122032 21.25 632.6136 391.99 5643.18 51874 144562 20.45 635.80934 385.25 5654.63 50200 163153 21.32 636.67236 383.46 5528.91 51874 183695 21.96 633.388 387.51 5616.21 51874 201470 23.99 638.09523 383.29 5882.17 50200 166062 24.90 641.6666 380.91 6029.38 51874 203959 23.71 646.15675 377.87 6521.70 50200 194742 25.39 651.88736 369.34 6448.27 51874 165450 25.17 651.22665 355.03 6813.09 42679 190427 22.21 654.79548 346.40 6877.74 38549 147520 20.99 654.65449 352.31 6583.48 42679 166109 19.72 659.91121 344.71 7008.99 41303 164804 20.83 653.37456 344.10 7331.04 42679 176121 19.17 645.67016 340.80 7672.79 41303 149173 19.63 650.11779 323.78 8222.61 42679 170451 19.93 658.91968 324.00 7622.42 42679 175412 19.79 662.42603 322.62 7945.26 41303 166340 21.26 667.60539 324.86 7442.08 42679 209292 20.17 666.14582 306.35 7823.13 41303 176079 18.32 664.27573 288.78 7908.25 42679 165432 16.71 676.48872 289.26 7906.50 38420 193754 16.06 680.1659 297.74 8545.72 34702 139788 15.02 678.99055 295.87 8799.81 38420 158378 15.44 677.61221 308.56 9063.37 37181 157995 14.86 672.24744 298.97 8899.95 38420 165045 13.66 669.48689 292.22 8952.02 37181 113321 14.08 668.19145 292.87 8883.29 38420 162527 13.36 658.29031 284.23 7539.07 38420 168683 14.95 659.15736 288.66 7842.62 37181 164104 14.39 660.34479 296.60 8592.10 38420 177656 12.85 668.77636 294.24 9116.55 37181 150257 11.28 667.01524 291.36 9181.43 38420 177723 12.47 669.37186 287.31 9358.83 39258 185473 12.01 672.58801 287.50 9306.58 35459 142068 14.66 669.36903 286.24 9786.16 39258 130617 17.34 654.958 282.62 10789.04 37992 161522 17.75 653.03433 276.93 10559.74 39258 195235 17.89 642.61224 261.40 10970.80 37992 151377 20.07 657.74554 256.20 10655.15 39258 177385 21.26 656.64312 256.94 10829.28 39258 172286 23.88 657.08444 264.47 10336.95 37992 158314 22.64 662.14989 311.56 10729.86 39258 142479 24.97 661.94064 293.65 10877.81 37992 167690 26.08 653.83966 283.74 11497.12 39258 149838 27.18 664.17629 284.59 10940.53 41499 135503 29.35 670.41666 300.85 10128.31 38822 158414 29.89 670.73615 286.70 10921.92 41499 167871 25.74 677.35214 279.96 10733.91 40160 161318 28.78 682.47259 275.29 10522.33 41499 157863 31.83 680.40158 285.37 10447.89 40160 141916 29.77 686.65088 282.15 10521.98 41499 163330 31.22 694.91907 274.52 11215.10 41499 164925 33.88 695.01497 273.68 10650.92 40160 152883 33.08 699.45908 270.40 10971.14 41499 175959 34.40 705.01325 265.99 10414.49 40160 171660 28.46 692.39885 271.89 10787.99 41499 176426 29.58 691.58396 265.93 10887.36 37310 151048 29.61 686.89767 262.02 10495.28 33699 133931 27.24 693.52192 263.27 9878.78 37310 144500 27.41 684.19211 260.75 10734.97 36106 141476 28.64 677.3194 272.06 10911.94 37310 136659 27.60 661.73269 270.74 10502.40 36106 138192 26.45 681.12618 267.71 10522.81 37310 143229 27.47 682.86649 272.66 9949.75 37310 147819 25.88 678.14703 282.48 8847.56 36106 139456 22.21 677.16558 283.32 9075.14 37310 150545 19.67 680.69632 276.25 9851.56 36106 144622 19.33 676.53337 275.99 10021.57 37310 147211 19.67 668.03667 281.76 9920.00 38130 145788 20.74 668.97155 295.68 10106.13 34440 127517 24.42 666.98498 294.35 10403.94 38130 134908 26.27 661.76808 302.86 9946.22 36900 135425 27.02 667.50585 314.48 9925.25 38130 144149 25.52 666.06849 321.54 9243.26 36900 134913 26.94 670.68352 313.57 8736.59 38130 150898 28.38 667.93756 310.05 8663.50 38130 138370 29.67 673.30419 318.71 7591.93 36900 146320 28.85 687.39311 316.75 8397.03 38130 158698 26.27 688.37189 319.25 8896.09 36900 147410 29.42 671.79872 333.30 8341.63 38130 155281 32.94 677.22114 356.86 8053.81 40016 144658 35.87 693.12414 359.58 7891.08 36143 131474 33.55 698.4465 341.56 7992.13 40016 143098 28.25 687.64631 328.21 8480.09 38725 140664 28.14 687.61812 355.41 8850.26 40016 142011 30.72 679.51819 356.91 8985.44 38725 139980 30.76 685.70631 350.75 9233.80 40016 149512 31.59 690.25711 358.99 9415.82 40016 146613 28.29 696.94325 378.86 9275.06 38725 141335 30.33 705.98803 379.09 9801.12 40016 146087 31.09 708.31175 390.20 9782.46 38725 143822 32.15 720.88209 407.67 10453.92 40016 156251 34.27 717.75784 414.50 10488.07 40887 160500 34.74 717.55138 404.73 10583.92 38250 146866 36.76 716.99835 405.98 10357.70 40887 152560 36.69 716.65894 404.85 10225.57 39569 154946 40.28 713.41968 383.95 10188.45 40887 148239 38.02 729.40358 391.78 10435.48 39569 150344 40.69 733.9712 398.44 10139.71 40887 157722 44.94 723.67358 400.13 10173.92 40887 155789 45.95 729.87932 405.40 10080.27 39569 149591 53.13 736.03188 420.21 10027.47 40887 157821 48.46 732.64932 439.06 10428.02 39569 152981 43.33 728.98133 442.97 10783.01 40887 165561 46.84 731.63834 424.08 10489.94 43020 162723 47.97 734.6898 423.43 10766.23 38857 153944 54.31 737.88677 434.35 10503.76 43020 157191 53.04 740.78089 429.14 10192.51 41632 151201 49.83 741.90176 422.90 10467.48 43020 154338 56.26 738.40627 430.30 10274.97 41632 151773 58.70 736.92209 424.75 10640.91 43020 159417 64.97 737.36337 437.77 10481.60 43020 158863 65.57 732.89592 455.94 10568.70 41632 153009 62.37 733.55081 470.11 10440.07 43020 157433 58.30 738.51387 476.67 10805.87 41632 152753 59.43 741.46149 509.42 10717.50 43020 160532 65.51 736.41252 549.43 10864.86 40384 171240 61.63 735.99137 555.52 10993.41 36476 149951 62.90 734.26023 557.22 11109.32 40384 158530 69.69 734.85552 611.85 11367.14 39081 152473 70.94 730.46576 676.77 11168.31 40384 156337 70.96 729.38976 597.90 11150.22 39081 155593 74.41 739.7233 633.09 11185.68 40384 164848 73.05 736.24305 631.56 11381.15 40384 163900 63.87 733.40158 600.15 11679.07 39081 159612 58.88 736.50854 586.65 12080.73 40384 165514 59.37 733.27274 626.83 12221.93 39081 162519 62.03 731.06448 629.51 12463.15 40384 169272 54.57 727.84077 630.35 12621.69 44750 166859 59.26 730.40812 665.10 12268.63 40419 152068 60.56 729.59849 655.89 12354.35 44750 164349 63.97 731.9837 680.01 13062.91 43307 161185 63.46 727.2243 668.31 13627.64 44750 162728 67.48 723.33322 655.71 13408.62 43307 158524 74.18 728.46526 665.27 13211.99 44750 165934 72.39 721.99641 664.53 13357.74 44750 163616 79.93 729.75831 710.65 13895.63 43307 159216 86.20 736.52441 754.48 13930.01 44750 167323 94.62 733.60152 808.31 13371.72 43307 166526 91.73 737.86398 803.62 13264.82 44750 176076 92.95 738.99614 887.78 12650.36 47816 187502 95.35 740.62804 924.28 12266.39 44731 164936 105.56 742.35983 971.06 12262.89 47816 172274 112.57 736.59008 912.02 12820.13 46274 170532 125.39 739.67615 889.13 12638.32 47816 174122 133.93 739.77171 889.54 11350.01 46274 165161 133.44 746.85967 941.17 11378.02 47816 172175 116.61 734.762 840.39 11543.55 47816 172293 103.90 725.37503 824.92 10850.66 46274 164661 76.65 735.5332 812.82 9325.01 47816 173427 57.44 733.90288 757.85 8829.04 46274 170402 41.02 725.90513 819.94 8776.39 47816 165221 41.74 715.5104 857.73 8000.86 47850 163411 39.16 720.74408 939.76 7062.93 43220 150358 47.98 718.75698 925.99 7608.92 47850 162993 49.79 719.95689 892.66 8168.12 46307 156878 59.16 716.1199 926.86 8500.33 47850 163197 69.68 718.00358 947.81 8447.00 46307 162195 64.09 727.08914 934.27 9171.61 47850 177127 71.06 722.45696 949.50 9496.28 47850 181363 69.46 726.53179 996.44 9712.28 46307 170555 75.82 731.11631 1043.51 9712.73 47850 179649 78.08 731.65889 1126.12 10344.84 46307 178388 74.30 728.91337 1135.01 10428.05 47850 183210
Names of X columns:
PRICE PRODUCTION GOLD DOWJONES RENEW_RISID RENEW_IND
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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