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Data X:
14 501 1002000 11 22000 20 40000 91.81 183620 77585 155170000 1303.2 2606400 2000 14 485 970000 11 22000 19 38000 91.98 183960 77585 155170000 -58.7 -117400 2000 15 464 928000 11 22000 18 36000 91.72 183440 77585 155170000 -378.9 -757800 2000 13 460 920460 11 22011 13 26013 90.27 180630.27 78302 156682302 175.6 351375.6 2001 8 467 934467 11 22011 17 34017 91.89 183871.89 78302 156682302 233.7 467633.7 2001 7 460 920460 9 18009 17 34017 92.07 184232.07 78302 156682302 706.8 1414306.8 2001 3 448 896448 8 16008 13 26013 92.92 185932.92 78224 156526224 -23.6 -47223.6 2001 3 443 886443 6 12006 14 28014 93.34 186773.34 78224 156526224 420.9 842220.9 2001 4 436 872436 7 14007 13 26013 93.6 187293.6 78224 156526224 722.1 1444922.1 2001 4 431 862431 8 16008 17 34017 92.41 184912.41 78178 156434178 1401.3 2804001.3 2001 0 484 968484 6 12006 17 34017 93.6 187293.6 78178 156434178 -94.9 -189894.9 2001 -4 510 1020510 5 10005 15 30015 93.77 187633.77 78178 156434178 1043.6 2088243.6 2001 -14 513 1026513 2 4002 9 18009 93.6 187293.6 77988 156053988 1300.1 2601500.1 2001 -18 503 1006503 3 6003 10 20010 93.6 187293.6 77988 156053988 721.1 1442921.1 2001 -8 471 942471 3 6003 9 18009 93.51 187113.51 77988 156053988 -45.6 -91245.6 2001 -1 471 942942 7 14014 14 28028 92.66 185505.32 77876 155907752 787.5 1576575 2002 1 476 952952 8 16016 18 36036 94.2 188588.4 77876 155907752 694.3 1389988.6 2002 2 475 950950 7 14014 18 36036 94.37 188928.74 77876 155907752 1054.7 2111509.4 2002 0 470 940940 7 14014 12 24024 94.45 189088.9 78432 157020864 821.9 1645443.8 2002 1 461 922922 6 12012 16 32032 94.62 189429.24 78432 157020864 1100.7 2203601.4 2002 0 455 910910 6 12012 12 24024 94.37 188928.74 78432 157020864 862.4 1726524.8 2002 -1 456 912912 7 14014 19 38038 93.43 187046.86 79025 158208050 1656.1 3315512.2 2002 -3 517 1035034 5 10010 13 26026 94.79 189769.58 79025 158208050 -174 -348348 2002 -3 525 1051050 5 10010 12 24024 94.88 189949.76 79025 158208050 1337.6 2677875.2 2002 -3 523 1047046 5 10010 13 26026 94.79 189769.58 79407 158972814 1394.9 2792589.8 2002 -4 519 1039038 4 8008 11 22022 94.62 189429.24 79407 158972814 915.7 1833231.4 2002 -8 509 1019018 4 8008 10 20020 94.71 189609.42 79407 158972814 -481.1 -963162.2 2002 -9 512 1025536 4 8012 16 32048 93.77 187821.31 79644 159526932 167.9 336303.7 2003 -13 519 1039557 1 2003 12 24036 95.73 191747.19 79644 159526932 208.2 417024.6 2003 -18 517 1035551 -1 -2003 6 12018 95.99 192267.97 79644 159526932 382.2 765546.6 2003 -11 510 1021530 3 6009 8 16024 95.82 191927.46 79381 159000143 1004 2011012 2003 -9 509 1019527 4 8012 6 12018 95.47 191226.41 79381 159000143 864.7 1731994.1 2003 -10 501 1003503 3 6009 8 16024 95.82 191927.46 79381 159000143 1052.9 2108958.7 2003 -13 507 1015521 2 4006 8 16024 94.71 189704.13 79536 159310608 1417.6 2839452.8 2003 -11 569 1139707 1 2003 9 18027 96.33 192948.99 79536 159310608 -197.7 -395993.1 2003 -5 580 1161740 4 8012 13 26039 96.5 193289.5 79536 159310608 1262.1 2527986.3 2003 -15 578 1157734 3 6009 8 16024 96.16 192608.48 79813 159865439 1147.2 2297841.6 2003 -6 565 1131695 5 10015 11 22033 96.33 192948.99 79813 159865439 700.2 1402500.6 2003 -6 547 1095641 6 12018 8 16024 96.33 192948.99 79813 159865439 45.3 90735.9 2003 -3 555 1112220 6 12024 10 20040 95.05 190480.2 80332 160985328 458.5 918834 2004 -1 562 1126248 6 12024 15 30060 96.84 194067.36 80332 160985328 610.2 1222840.8 2004 -3 561 1124244 6 12024 12 24048 96.92 194227.68 80332 160985328 786.4 1575945.6 2004 -4 555 1112220 6 12024 13 26052 97.44 195269.76 81434 163193736 787.2 1577548.8 2004 -6 544 1090176 5 10020 12 24048 97.78 195951.12 81434 163193736 1040 2084160 2004 0 537 1076148 6 12024 15 30060 97.69 195770.76 81434 163193736 324.1 649496.4 2004 -4 543 1088172 5 10020 13 26052 96.67 193726.68 82167 164662668 1343 2691372 2004 -2 594 1190376 6 12024 13 26052 98.29 196973.16 82167 164662668 -501.2 -1004404.8 2004 -2 611 1224444 5 10020 16 32064 98.2 196792.8 82167 164662668 800.4 1604001.6 2004 -6 613 1228452 7 14028 14 28056 98.71 197814.84 82816 165963264 916.7 1837066.8 2004 -7 611 1224444 4 8016 12 24048 98.54 197474.16 82816 165963264 695.8 1394383.2 2004 -6 594 1190376 5 10020 15 30060 98.2 196792.8 82816 165963264 28 56112 2004 -6 595 1192975 6 12030 14 28070 96.92 194324.6 83000 166415000 495.6 993678 2005 -3 591 1184955 6 12030 19 38095 99.06 198615.3 83000 166415000 366.2 734231 2005 -2 589 1180945 5 10025 16 32080 99.65 199798.25 83000 166415000 633 1269165 2005 -5 584 1170920 3 6015 16 32080 99.82 200139.1 83251 166918255 848.3 1700841.5 2005 -11 573 1148865 2 4010 11 22055 99.99 200479.95 83251 166918255 472.2 946761 2005 -11 567 1136835 3 6015 13 26065 100.33 201161.65 83251 166918255 357.8 717389 2005 -11 569 1140845 3 6015 12 24060 99.31 199116.55 83591 167599955 824.3 1652721.5 2005 -10 621 1245105 2 4010 11 22055 101.1 202705.5 83591 167599955 -880.1 -1764600.5 2005 -14 629 1261145 0 0 6 12030 101.1 202705.5 83591 167599955 1066.8 2138934 2005 -8 628 1259140 4 8020 9 18045 100.93 202364.65 83910 168239550 1052.8 2110864 2005 -9 612 1227060 4 8020 6 12030 100.85 202204.25 83910 168239550 -32.1 -64360.5 2005 -5 595 1192975 5 10025 15 30075 100.93 202364.65 83910 168239550 -1331.4 -2669457 2005 -1 597 1197582 6 12036 17 34102 99.6 199797.6 84599 169705594 -767.1 -1538802.6 2006 -2 593 1189558 6 12036 13 26078 101.88 204371.28 84599 169705594 -236.7 -474820.2 2006 -5 590 1183540 5 10030 12 24072 101.81 204230.86 84599 169705594 -184.9 -370909.4 2006 -4 580 1163480 5 10030 13 26078 102.38 205374.28 85275 171061650 -143.4 -287660.4 2006 -6 574 1151444 3 6018 10 20060 102.74 206096.44 85275 171061650 493.9 990763.4 2006 -2 573 1149438 5 10030 14 28084 102.82 206256.92 85275 171061650 549.7 1102698.2 2006 -2 573 1149438 5 10030 13 26078 101.72 204050.32 85608 171729648 982.7 1971296.2 2006 -2 620 1243720 5 10030 10 20060 103.47 207560.82 85608 171729648 -856.3 -1717737.8 2006 -2 626 1255756 3 6018 11 22066 102.98 206577.88 85608 171729648 967 1939802 2006 2 620 1243720 6 12036 12 24072 102.68 205976.08 86303 173123818 659.4 1322756.4 2006 1 588 1179528 6 12036 7 14042 102.9 206417.4 86303 173123818 577.2 1157863.2 2006 -8 566 1135396 4 8024 11 22066 103.03 206678.18 86303 173123818 -213.1 -427478.6 2006 -1 557 1117899 6 12042 9 18063 101.29 203289.03 87115 174839805 17.7 35523.9 2007 1 561 1125927 5 10035 13 26091 103.69 208105.83 87115 174839805 390.1 782930.7 2007 -1 549 1101843 4 8028 12 24084 103.68 208085.76 87115 174839805 509.3 1022165.1 2007 2 532 1067724 5 10035 5 10035 104.2 209129.4 87931 176477517 410 822870 2007 2 526 1055682 5 10035 13 26091 104.08 208888.56 87931 176477517 212.5 426487.5 2007 1 511 1025577 4 8028 11 22077 104.16 209049.12 87931 176477517 818 1641726 2007 -1 499 1001493 3 6021 8 16056 103.05 206821.35 88164 176945148 422.7 848358.9 2007 -2 555 1113885 2 4014 8 16056 104.66 210052.62 88164 176945148 -158 -317106 2007 -2 565 1133955 3 6021 8 16056 104.46 209651.22 88164 176945148 427.2 857390.4 2007 -1 542 1087794 2 4014 8 16056 104.95 210634.65 88792 178205544 243.4 488503.8 2007 -8 527 1057689 -1 -2007 0 0 105.85 212440.95 88792 178205544 -419.3 -841535.1 2007 -4 510 1023570 0 0 3 6021 106.23 213203.61 88792 178205544 -1459.8 -2929818.6 2007 -6 514 1032112 -2 -4016 0 0 104.86 210558.88 89263 179240104 -1389.8 -2790718.4 2008 -3 517 1038136 1 2008 -1 -2008 107.44 215739.52 89263 179240104 -2.1 -4216.8 2008 -3 508 1020064 -2 -4016 -1 -2008 108.23 217325.84 89263 179240104 -938.6 -1884708.8 2008 -7 493 989944 -2 -4016 -4 -8032 108.45 217767.6 89881 180481048 -839.9 -1686519.2 2008 -9 490 983920 -2 -4016 1 2008 109.39 219655.12 89881 180481048 -297.6 -597580.8 2008 -11 469 941752 -6 -12048 -1 -2008 110.15 221181.2 89881 180481048 -376.3 -755610.4 2008 -13 478 959824 -4 -8032 0 0 109.13 219133.04 90120 180960960 -79.4 -159435.2 2008 -11 528 1060224 -2 -4016 -1 -2008 110.28 221442.24 90120 180960960 -2091.3 -4199330.4 2008 -9 534 1072272 0 0 6 12048 110.17 221221.36 90120 180960960 -1023 -2054184 2008 -17 518 1040144 -5 -10040 0 0 109.99 220859.92 89703 180123624 -765.6 -1537324.8 2008 -22 506 1016048 -4 -8032 -3 -6024 109.26 219394.08 89703 180123624 -1592.3 -3197338.4 2008 -25 502 1008016 -5 -10040 -3 -6024 109.11 219092.88 89703 180123624 -1588.8 -3190310.4 2008 -20 516 1036644 -1 -2009 4 8036 107.06 215083.54 87818 176426362 -1318 -2647862 2009 -24 528 1060752 -2 -4018 1 2009 109.53 220045.77 87818 176426362 -402.4 -808421.6 2009 -24 533 1070797 -4 -8036 0 0 108.92 218820.28 87818 176426362 -814.5 -1636330.5 2009 -22 536 1076824 -1 -2009 -4 -8036 109.24 219463.16 86273 173322457 -98.4 -197685.6 2009 -19 537 1078833 1 2009 -2 -4018 109.12 219222.08 86273 173322457 -305.9 -614553.1 2009 -18 524 1052716 1 2009 3 6027 109 218981 86273 173322457 -18.4 -36965.6 2009 -17 536 1076824 -2 -4018 2 4018 107.23 215425.07 86316 173408844 610.3 1226092.7 2009 -11 587 1179283 1 2009 5 10045 109.49 219965.41 86316 173408844 -917.3 -1842855.7 2009 -11 597 1199373 1 2009 6 12054 109.04 219061.36 86316 173408844 88.4 177595.6 2009 -12 581 1167229 3 6027 6 12054 109.02 219021.18 87234 175253106 -740.2 -1487061.8 2009 -10 564 1133076 3 6027 3 6027 109.23 219443.07 87234 175253106 29.3 58863.7 2009 -15 558 1121022 1 2009 4 8036 109.46 219905.14 87234 175253106 -893.2 -1794438.8 2009 -15 575 1155750 1 2010 7 14070 107.9 216879 87885 176648850 -1030.2 -2070702 2010 -15 580 1165800 0 0 5 10050 110.42 221944.2 87885 176648850 -403.4 -810834 2010 -13 575 1155750 2 4020 6 12060 110.98 223069.8 87885 176648850 -46.9 -94269 2010 -8 563 1131630 2 4020 1 2010 111.48 224074.8 88003 176886030 -321.2 -645612 2010 -13 552 1109520 -1 -2010 3 6030 111.88 224878.8 88003 176886030 -239.9 -482199 2010 -9 537 1079370 1 2010 6 12060 111.89 224898.9 88003 176886030 640.9 1288209 2010 -7 545 1095450 0 0 0 0 109.85 220798.5 88910 178709100 511.6 1028316 2010 -4 601 1208010 1 2010 3 6030 112.1 225321 88910 178709100 -665.1 -1336851 2010 -4 604 1214040 1 2010 4 8040 112.24 225602.4 88910 178709100 657.7 1321977 2010 -2 586 1177860 3 6030 7 14070 112.39 225903.9 89397 179687970 -207.7 -417477 2010 0 564 1133640 2 4020 6 12060 112.52 226165.2 89397 179687970 -885.2 -1779252 2010 -2 549 1103490 0 0 6 12060 113.16 227451.6 89397 179687970 -1595.8 -3207558 2010 -3 551 1108061 0 0 6 12066 111.84 224910.24 89813 180613943 -1374.9 -2764923.9 2011 1 556 1118116 3 6033 6 12066 114.33 229917.63 89813 180613943 -316.6 -636682.6 2011 -2 548 1102028 -2 -4022 2 4022 114.82 230903.02 89813 180613943 -283.4 -569917.4 2011 -1 540 1085940 0 0 2 4022 115.2 231667.2 90539 182073929 -175.8 -353533.8 2011 1 531 1067841 1 2011 2 4022 115.4 232069.4 90539 182073929 -694.2 -1396036.2 2011 -3 521 1047731 -1 -2011 3 6033 115.74 232753.14 90539 182073929 -249.9 -502548.9 2011 -4 519 1043709 -2 -4022 -1 -2011 114.19 229636.09 90688 182373568 268.2 539350.2 2011 -9 572 1150292 -1 -2011 -4 -8044 115.94 233155.34 90688 182373568 -2105.1 -4233356.1 2011 -9 581 1168391 -1 -2011 4 8044 116.03 233336.33 90688 182373568 -762.8 -1533990.8 2011 -7 563 1132193 1 2011 5 10055 116.24 233758.64 90691 182379601 -117.1 -235488.1 2011 -14 548 1102028 -2 -4022 3 6033 116.66 234603.26 90691 182379601 -1094.4 -2200838.4 2011 -12 539 1083929 -5 -10055 -1 -2011 116.79 234864.69 90691 182379601 -2095.2 -4213447.2 2011 -16 541 1088492 -5 -10060 -4 -8048 115.48 232345.76 90645 182377740 -1587.6 -3194251.2 2012 -20 562 1130744 -6 -12072 0 0 118.16 237737.92 90645 182377740 -528 -1062336 2012 -12 559 1124708 -4 -8048 -1 -2012 118.38 238180.56 90645 182377740 -324.2 -652290.4 2012 -12 546 1098552 -3 -6036 -1 -2012 118.51 238442.12 90861 182812332 -276.1 -555513.2 2012 -10 536 1078432 -3 -6036 3 6036 118.42 238261.04 90861 182812332 -139.1 -279869.2 2012 -10 528 1062336 -1 -2012 2 4024 118.24 237898.88 90861 182812332 268 539216 2012 -13 530 1066360 -2 -4024 -4 -8048 116.47 234337.64 90401 181886812 570.5 1147846 2012 -16 582 1170984 -3 -6036 -3 -6036 118.96 239347.52 90401 181886812 -316.5 -636798 2012
Names of X columns:
i w w_t f f_t s s_t c c_t b b_t h h_t t
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
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3
4
5
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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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