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Data X:
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38.84 0 0 1 0 23185 1466998 64.87 6.92 0 0 0 1 22777 1466998 64.87 12.67 0 0 0 1 23508 1466998 64.87 44.39 0 0 0 1 23193 1466998 64.87 3.65 0 0 0 1 23006 1466998 64.87 5.23 0 0 0 1 22332 1466998 63.34 9.03 1 0 0 0 22347 1466998 63.34 11.54 1 0 0 0 23061 1466998 63.34 48.32 1 0 0 0 22887 1466998 63.34 19.97 0 1 0 0 22890 1466998 63.34 11.5 0 1 0 0 22701 1466998 63.34 10.07 0 1 0 0 22467 1466998 63.34 12.09 0 1 0 0 22357 1466998 63.34 5.28 0 1 0 0 22443 1466998 63.34 3.97 0 1 0 0 22824 1466998 63.34 8.98 0 0 1 0 22906 1466998 63.34 42.53 0 0 1 0 23059 1466998 63.34 7.36 0 0 1 0 23055 1466998 63.34 5.56 0 0 1 0 22564 1466998 63.34 23.48 0 0 0 1 18570 1466998 63.34 8.98 0 0 0 0 20329 1466998 63.34 42.53 0 0 0 0 19279 1466998 63.34 3.97 0 0 0 0 19541 1466998 63.34 19.97 0 0 0 0 19517 1466998 63.34 7.36 0 0 0 0 6519 466184 10.57 4.97 1 0 0 0 7169 466184 10.57 4.18 1 0 0 0 8107 466184 10.57 5.68 1 0 0 0 10668 466184 10.57 49.13 0 1 0 0 6650 466184 10.57 11.9 0 1 0 0 5726 466184 10.57 3.19 0 1 0 0 5224 466184 10.57 5.46 0 0 1 0 6297 466184 10.57 19.11 0 0 1 0 5011 466184 10.57 10.44 0 0 1 0 5075 466184 10.57 8.3 0 0 1 0 5434 466184 10.57 12.62 0 0 1 0 11758 466184 10.57 67.99 0 0 0 1 4531 466184 10.57 12.79 0 0 0 1 5373 466184 10.57 6.05 0 0 0 1 6343 466184 10.57 35.71 0 0 0 1 20051 466184 10.57 25.56 0 0 0 1 5482 466184 10.57 5.67 0 0 0 1 5066 466184 10.59 5.23 1 0 0 0 5040 466184 10.59 9.06 0 1 0 0 5100 466184 10.59 3.56 0 1 0 0 4679 466184 10.59 11.96 0 1 0 0 10940 466184 10.59 44.39 0 1 0 0 6035 466184 10.59 3.22 0 1 0 0 5364 466184 10.59 4.78 0 0 1 0 4424 466184 10.59 3.65 0 0 1 0 4486 466184 10.59 10.41 0 0 1 0 4962 466184 10.59 18.63 0 0 1 0 10445 466184 10.59 64.87 0 0 1 0 5973 466184 10.59 5.69 0 0 1 0 5415 466184 10.59 12.67 0 0 0 1 17792 466184 10.59 38.84 0 0 0 1 5581 466184 10.59 11.59 0 0 0 1 4997 466184 10.59 6.92 0 0 0 1 6893 466184 10.59 29.97 0 0 0 1 10181 466184 10.07 42.53 1 0 0 0 7007 466184 10.07 11.5 1 0 0 0 6621 466184 10.07 9.03 0 1 0 0 7309 466184 10.07 5.56 0 1 0 0 6114 466184 10.07 3.97 0 1 0 0 5521 466184 10.07 11.54 0 1 0 0 5263 466184 10.07 19.97 0 1 0 0 5400 466184 10.07 7.36 0 0 1 0 6141 466184 10.07 5.28 0 0 1 0 5736 466184 10.07 23.48 0 0 1 0 16104 466184 10.07 48.32 0 0 1 0 10810 466184 10.07 63.34 0 0 0 1 5057 466184 10.07 8.98 0 0 0 1 5732 466184 10.07 12.09 0 0 0 1 4000 466184 10.07 11.5 0 0 0 0 4000 466184 10.07 23.48 0 0 0 0 4200 466184 10.07 48.32 0 0 0 0 3551 143920 12.79 8.3 1 0 0 0 4025 143920 12.79 12.62 1 0 0 0 5591 143920 12.79 4.18 0 1 0 0 3868 143920 12.79 6.05 0 1 0 0 3566 143920 12.79 10.57 0 1 0 0 4525 143920 12.79 35.71 0 1 0 0 3752 143920 12.79 5.67 0 0 1 0 3182 143920 12.79 10.44 0 0 1 0 6152 143920 12.79 67.99 0 0 1 0 3548 143920 12.79 5.68 0 0 1 0 6876 143920 12.79 49.13 0 0 1 0 3199 143920 12.79 11.9 0 0 0 1 3386 143920 12.79 3.19 0 0 0 1 3411 143920 12.79 5.46 0 0 0 1 6892 143920 12.79 25.56 0 0 0 1 4920 143920 12.79 19.11 0 0 0 1 3193 143920 12.79 4.97 0 0 0 1 3054 143920 11.96 3.22 1 0 0 0 3262 143920 11.96 10.41 0 1 0 0 3509 143920 11.96 9.06 0 1 0 0 3471 143920 11.96 3.56 0 1 0 0 3101 143920 11.96 5.69 0 1 0 0 5956 143920 11.96 38.84 0 1 0 0 6232 143920 11.96 44.39 0 0 1 0 5456 143920 11.96 4.78 0 0 1 0 3186 143920 11.96 6.92 0 0 1 0 3751 143920 11.96 29.97 0 0 1 0 2973 143920 11.96 5.23 0 0 1 0 5548 143920 11.96 18.63 0 0 0 1 3219 143920 11.96 10.59 0 0 0 1 6595 143920 11.96 64.87 0 0 0 1 4886 143920 11.96 12.67 0 0 0 1 3082 143920 11.96 11.59 0 0 0 1 3516 143920 11.96 3.65 0 0 0 1 3807 143920 12.09 8.98 1 0 0 0 3607 143920 12.09 7.36 1 0 0 0 3163 143920 12.09 10.52 1 0 0 0 4981 143920 12.09 48.32 0 1 0 0 3276 143920 12.09 10.07 0 1 0 0 3278 143920 12.09 9.03 0 1 0 0 3850 143920 12.09 23.48 0 1 0 0 3439 143920 12.09 3.97 0 1 0 0 5545 143920 12.09 42.53 0 0 1 0 4749 143920 12.09 19.97 0 0 1 0 3656 143920 12.09 11.5 0 0 1 0 3520 143920 12.09 5.28 0 0 1 0 4392 143920 12.09 5.56 0 0 1 0 3057 143920 12.09 11.54 0 0 1 0 6542 143920 12.09 63.34 0 0 0 1 2785 143920 12.09 9.03 0 0 0 0 3057 143920 12.09 11.54 0 0 0 0 4379 143920 12.09 5.56 0 0 0 0 23934 787601 25.56 6.05 1 0 0 0 23625 787601 25.56 5.67 1 0 0 0 26185 787601 25.56 49.13 0 1 0 0 23777 787601 25.56 10.44 0 1 0 0 24586 787601 25.56 12.62 0 1 0 0 25439 787601 25.56 4.18 0 1 0 0 24037 787601 25.56 12.79 0 1 0 0 25403 787601 25.56 10.57 0 0 1 0 25133 787601 25.56 35.71 0 0 1 0 24023 787601 25.56 5.46 0 0 1 0 23901 787601 25.56 4.97 0 0 1 0 24892 787601 25.56 19.11 0 0 1 0 24560 787601 25.56 8.3 0 0 0 1 24226 787601 25.56 5.68 0 0 0 1 24885 787601 25.56 11.9 0 0 0 1 25466 787601 25.56 67.99 0 0 0 1 24903 787601 25.56 3.19 0 0 0 1 23761 787601 38.84 10.41 1 0 0 0 23868 787601 38.84 3.56 0 1 0 0 26118 787601 38.84 64.87 0 1 0 0 25120 787601 38.84 5.69 0 1 0 0 26119 787601 38.84 10.59 0 1 0 0 25440 787601 38.84 11.59 0 1 0 0 24206 787601 38.84 6.92 0 0 1 0 25312 787601 38.84 18.63 0 0 1 0 24499 787601 38.84 3.65 0 0 1 0 24330 787601 38.84 5.23 0 0 1 0 24217 787601 38.84 3.22 0 0 1 0 25047 787601 38.84 9.06 0 0 0 1 25817 787601 38.84 29.97 0 0 0 1 25466 787601 38.84 12.67 0 0 0 1 25410 787601 38.84 11.96 0 0 0 1 26246 787601 38.84 4.78 0 0 0 1 25718 787601 38.84 44.39 0 0 0 1 26543 787601 48.32 7.36 1 0 0 0 26723 787601 48.32 10.07 1 0 0 0 26700 787601 48.32 5.56 0 1 0 0 24743 787601 48.32 9.03 0 1 0 0 25520 787601 48.32 23.48 0 1 0 0 25298 787601 48.32 8.98 0 1 0 0 26382 787601 48.32 42.53 0 1 0 0 25719 787601 48.32 11.5 0 0 1 0 24547 787601 48.32 5.28 0 0 1 0 25640 787601 48.32 12.09 0 0 1 0 26103 787601 48.32 63.34 0 0 1 0 24911 787601 48.32 3.97 0 0 1 0 25199 787601 48.32 11.54 0 0 1 0 25308 787601 48.32 19.97 0 0 0 1 17000 787601 48.32 10.07 0 0 0 0 12000 787601 48.32 11.5 0 0 0 0 20000 787601 48.32 23.48 0 0 0 0 3915 146788 8.3 11.9 1 0 0 0 3229 146788 8.3 10.57 1 0 0 0 6671 146788 8.3 35.71 0 1 0 0 5937 146788 8.3 25.56 0 1 0 0 3639 146788 8.3 5.46 0 1 0 0 4274 146788 8.3 19.11 0 1 0 0 3781 146788 8.3 12.62 0 0 1 0 5612 146788 8.3 67.99 0 0 1 0 4498 146788 8.3 4.18 0 0 1 0 3520 146788 8.3 12.79 0 0 1 0 6323 146788 8.3 49.13 0 0 1 0 3622 146788 8.3 3.19 0 0 1 0 4085 146788 8.3 6.05 0 0 0 1 3978 146788 8.3 5.67 0 0 0 1 3788 146788 8.3 4.97 0 0 0 1 3973 146788 8.3 10.44 0 0 0 1 3268 146788 8.3 5.68 0 0 0 1 6852 146788 7.36 10.07 1 0 0 0 6237 146788 7.36 5.28 1 0 0 0 9194 146788 7.36 63.34 0 1 0 0 9177 146788 7.36 23.48 0 1 0 0 6170 146788 7.36 8.98 0 1 0 0 6295 146788 7.36 19.97 0 1 0 0 5878 146788 7.36 9.03 0 1 0 0 7172 146788 7.36 48.32 0 1 0 0 5741 146788 7.36 3.97 0 0 1 0 7093 146788 7.36 5.56 0 0 1 0 5774 146788 7.36 12.09 0 0 1 0 5690 146788 7.36 11.54 0 0 1 0 7717 146788 7.36 42.53 0 0 1 0 6511 146788 7.36 11.5 0 0 0 1 6989 146788 7.36 42.53 0 0 0 0 9006 146788 7.36 63.34 0 0 0 0 6052 146788 7.36 3.97 0 0 0 0 5094 146788 7.36 8.98 0 0 0 0 6198 146788 7.36 19.97 0 0 0 0 8845 146788 7.36 23.48 0 0 0 0 6219 127152 5.67 5.46 1 0 0 0 5984 127152 5.67 3.19 1 0 0 0 7303 127152 5.67 19.11 0 1 0 0 6887 127152 5.67 12.62 0 1 0 0 8083 127152 5.67 8.3 0 1 0 0 18978 127152 5.67 67.99 0 1 0 0 25222 127152 5.67 49.13 0 1 0 0 6093 127152 5.67 11.9 0 0 1 0 7206 127152 5.67 10.57 0 0 1 0 8070 127152 5.67 35.71 0 0 1 0 16129 127152 5.67 25.56 0 0 1 0 7646 127152 5.67 4.97 0 0 1 0 5415 127152 5.67 10.44 0 0 0 1 10480 127152 5.67 4.18 0 0 0 1 5998 127152 5.67 5.68 0 0 0 1 6289 127152 5.67 12.79 0 0 0 1 6146 127152 5.67 6.05 0 0 0 1 12308 127152 9.06 64.87 1 0 0 0 7128 127152 9.06 5.69 1 0 0 0 7653 127152 9.06 12.67 0 1 0 0 10130 127152 9.06 38.84 0 1 0 0 8741 127152 9.06 3.65 0 1 0 0 7719 127152 9.06 6.92 0 1 0 0 8167 127152 9.06 4.78 0 1 0 0 7786 127152 9.06 18.63 0 0 1 0 8091 127152 9.06 5.23 0 0 1 0 8089 127152 9.06 3.56 0 0 1 0 7219 127152 9.06 10.59 0 0 1 0 7373 127152 9.06 11.96 0 0 1 0 20659 127152 9.06 44.39 0 0 0 1 7158 127152 9.06 11.59 0 0 0 1 7503 127152 9.06 3.22 0 0 0 1 7654 127152 9.06 29.97 0 0 0 1 7747 127152 9.06 10.41 0 0 0 1 8631 127152 9.03 19.97 1 0 0 0 6867 127152 9.03 8.98 1 0 0 0 17989 127152 9.03 42.53 1 0 0 0 6798 127152 9.03 11.5 0 1 0 0 7485 127152 9.03 5.56 0 1 0 0 7085 127152 9.03 3.97 0 1 0 0 7235 127152 9.03 11.54 0 1 0 0 8534 127152 9.03 63.34 0 0 1 0 7785 127152 9.03 48.32 0 0 1 0 7017 127152 9.03 5.28 0 0 1 0 5951 127152 9.03 10.07 0 0 1 0 6709 127152 9.03 23.48 0 0 1 0 6999 127152 9.03 12.09 0 0 0 1 6175 127152 9.03 7.36 0 0 0 1 5927 127152 9.03 11.54 0 0 0 0 6703 127152 9.03 5.56 0 0 0 0 6118 127152 9.03 12.09 0 0 0 0 4565 183693 4.97 3.19 1 0 0 0 6178 183693 4.97 25.56 1 0 0 0 5389 183693 4.97 5.67 0 1 0 0 5079 183693 4.97 5.46 0 1 0 0 7382 183693 4.97 67.99 0 1 0 0 4305 183693 4.97 8.3 0 1 0 0 4216 183693 4.97 5.68 0 1 0 0 7849 183693 4.97 49.13 0 0 1 0 4248 183693 4.97 10.57 0 0 1 0 4238 183693 4.97 12.79 0 0 1 0 4746 183693 4.97 35.71 0 0 1 0 4227 183693 4.97 6.05 0 0 1 0 4946 183693 4.97 19.11 0 0 0 1 4234 183693 4.97 10.44 0 0 0 1 4379 183693 4.97 12.62 0 0 0 1 5464 183693 4.97 4.18 0 0 0 1 4240 183693 4.97 11.9 0 0 0 1 5465 183693 5.23 3.56 1 0 0 0 5634 183693 5.23 38.84 1 0 0 0 3984 183693 5.23 11.96 0 1 0 0 6957 183693 5.23 44.39 0 1 0 0 4492 183693 5.23 6.92 0 1 0 0 3863 183693 5.23 3.22 0 1 0 0 3845 183693 5.23 3.65 0 1 0 0 3768 183693 5.23 10.41 0 0 1 0 6071 183693 5.23 64.87 0 0 1 0 3794 183693 5.23 10.59 0 0 1 0 4078 183693 5.23 12.67 0 0 1 0 3927 183693 5.23 5.69 0 0 1 0 3931 183693 5.23 11.59 0 0 0 1 5368 183693 5.23 4.78 0 0 0 1 5142 183693 5.23 29.97 0 0 0 1 5165 183693 5.23 18.63 0 0 0 1 4432 183693 5.23 9.06 0 0 0 1 5082 183693 5.28 11.5 1 0 0 0 6087 183693 5.28 48.32 1 0 0 0 4434 183693 5.28 10.07 1 0 0 0 4360 183693 5.28 12.09 0 1 0 0 5634 183693 5.28 9.03 0 1 0 0 7836 183693 5.28 42.53 0 1 0 0 5394 183693 5.28 8.98 0 1 0 0 4327 183693 5.28 10.52 0 0 1 0 4142 183693 5.28 7.36 0 0 1 0 5251 183693 5.28 19.97 0 0 1 0 4951 183693 5.28 5.56 0 0 1 0 4565 183693 5.28 3.97 0 0 1 0 4463 183693 5.28 23.48 0 0 1 0 5922 183693 5.28 63.34 0 0 1 0 4581 183693 5.28 11.54 0 0 0 1 7566 147239 5.46 25.56 1 0 0 0 5500 147239 5.46 10.44 1 0 0 0 5745 147239 5.46 12.62 0 1 0 0 6924 147239 5.46 4.18 0 1 0 0 5354 147239 5.46 5.68 0 1 0 0 5563 147239 5.46 12.79 0 1 0 0 5369 147239 5.46 11.9 0 1 0 0 5658 147239 5.46 3.19 0 0 1 0 5215 147239 5.46 6.05 0 0 1 0 5824 147239 5.46 5.67 0 0 1 0 6667 147239 5.46 19.11 0 0 1 0 7795 147239 5.46 67.99 0 0 1 0 5490 147239 5.46 4.97 0 0 0 1 5232 147239 5.46 8.3 0 0 0 1 7739 147239 5.46 49.13 0 0 0 1 5404 147239 5.46 10.57 0 0 0 1 6045 147239 5.46 35.71 0 0 0 1 6012 147239 6.92 11.96 1 0 0 0 6287 147239 6.92 29.97 0 1 0 0 5185 147239 6.92 3.22 0 1 0 0 8080 147239 6.92 64.87 0 1 0 0 7229 147239 6.92 18.63 0 1 0 0 5602 147239 6.92 12.67 0 1 0 0 5329 147239 6.92 10.59 0 1 0 0 5401 147239 6.92 11.59 0 0 1 0 8283 147239 6.92 44.39 0 0 1 0 6359 147239 6.92 4.78 0 0 1 0 5457 147239 6.92 3.65 0 0 1 0 5654 147239 6.92 10.41 0 0 1 0 6391 147239 6.92 5.23 0 0 0 1 5765 147239 6.92 9.06 0 0 0 1 6707 147239 6.92 3.56 0 0 0 1 8214 147239 6.92 38.84 0 0 0 1 5621 147239 6.92 5.69 0 0 0 1 6387 147239 8.98 23.48 1 0 0 0 8299 147239 8.98 63.34 1 0 0 0 6526 147239 8.98 3.97 0 1 0 0 5514 147239 8.98 10.52 0 1 0 0 6659 147239 8.98 19.97 0 1 0 0 6023 147239 8.98 11.5 0 1 0 0 5701 147239 8.98 10.07 0 1 0 0 6628 147239 8.98 5.56 0 1 0 0 5845 147239 8.98 12.09 0 1 0 0 5778 147239 8.98 9.03 0 0 1 0 5668 147239 8.98 11.54 0 0 1 0 5982 147239 8.98 7.36 0 0 1 0 8294 147239 8.98 42.53 0 0 1 0 5970 147239 8.98 5.28 0 0 1 0 7440 147239 8.98 48.32 0 0 0 1 5385 147239 8.98 7.36 0 0 0 0 6226 147239 8.98 19.97 0 0 0 0 6905 147239 8.98 42.53 0 0 0 0 7566 147239 8.98 63.34 0 0 0 0 6033 147239 8.98 3.97 0 0 0 0 3338 162295 10.44 10.57 1 0 0 0 2778 162295 10.44 11.9 1 0 0 0 2876 162295 10.44 3.19 0 1 0 0 3059 162295 10.44 5.67 0 1 0 0 2827 162295 10.44 4.97 0 1 0 0 3819 162295 10.44 12.62 0 1 0 0 3319 162295 10.44 8.3 0 1 0 0 5529 162295 10.44 4.18 0 0 1 0 2791 162295 10.44 6.05 0 0 1 0 6521 162295 10.44 49.13 0 0 1 0 2959 162295 10.44 5.46 0 0 1 0 4378 162295 10.44 35.71 0 0 1 0 6042 162295 10.44 25.56 0 0 0 1 3715 162295 10.44 19.11 0 0 0 1 6219 162295 10.44 67.99 0 0 0 1 2890 162295 10.44 5.68 0 0 0 1 3134 162295 10.44 12.79 0 0 0 1 3544 162295 10.41 10.59 1 0 0 0 3915 162295 10.41 12.67 1 0 0 0 3139 162295 10.41 11.59 0 1 0 0 2989 162295 10.41 6.92 0 1 0 0 2856 162295 10.41 3.22 0 1 0 0 5619 162295 10.41 29.97 0 1 0 0 3955 162295 10.41 18.63 0 1 0 0 3027 162295 10.41 5.69 0 1 0 0 3760 162295 10.41 9.06 0 0 1 0 6323 162295 10.41 38.84 0 0 1 0 3362 162295 10.41 11.96 0 0 1 0 6263 162295 10.41 44.39 0 0 1 0 5720 162295 10.41 4.78 0 0 0 1 3035 162295 10.41 3.65 0 0 0 1 6509 162295 10.41 64.87 0 0 0 1 3123 162295 10.41 5.23 0 0 0 1 3332 162295 10.41 3.56 0 0 0 1 3298 162295 11.54 3.97 1 0 0 0 4579 162295 11.54 23.48 1 0 0 0 2963 162295 11.54 8.98 1 0 0 0 5861 162295 11.54 42.53 0 1 0 0 4549 162295 11.54 7.36 0 1 0 0 6211 162295 11.54 48.32 0 1 0 0 2942 162295 11.54 5.28 0 1 0 0 3181 162295 11.54 12.09 0 1 0 0 5019 162295 11.54 5.56 0 1 0 0 6590 162295 11.54 63.34 0 0 1 0 4528 162295 11.54 19.97 0 0 1 0 3744 162295 11.54 11.5 0 0 1 0 3096 162295 11.54 10.07 0 0 1 0 2893 162295 11.54 9.03 0 0 0 1 3946 162295 11.54 5.56 0 0 0 0 2838 162295 11.54 12.09 0 0 0 0 2804 162295 11.54 9.03 0 0 0 0
Names of X columns:
Toeschouwers inwonersaantal_thuis reputatiecoef_thuisploeg reputatiecoef_bezoeker juli-aug sep-nov dec-feb ma-eind
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Include Quarterly 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 mywarning <- '' par1 <- as.numeric(par1) if(is.na(par1)) { par1 <- 1 mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.' } if (par4=='') par4 <- 0 par4 <- as.numeric(par4) if (par5=='') par5 <- 0 par5 <- as.numeric(par5) x <- na.omit(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'){ (n <- n -1) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par3 == 'Seasonal Differences (s=12)'){ (n <- n - 12) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+12,j] - x[i,j] } } x <- x2 } if (par3 == 'First and Seasonal Differences (s=12)'){ (n <- n -1) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 (n <- n - 12) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+12,j] - x[i,j] } } x <- x2 } if(par4 > 0) { x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep=''))) for (i in 1:(n-par4)) { for (j in 1:par4) { x2[i,j] <- x[i+par4-j,par1] } } x <- cbind(x[(par4+1):n,], x2) n <- n - par4 } if(par5 > 0) { x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep=''))) for (i in 1:(n-par5*12)) { for (j in 1:par5) { x2[i,j] <- x[i+par5*12-j*12,par1] } } x <- cbind(x[(par5*12+1):n,], x2) n <- n - par5*12 } 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[n,])) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x (k <- length(x[n,])) head(x) 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, mywarning) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+')) a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+')) a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' ')) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[2],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' ')) 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,formatC(signif(mysum$sigma,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') if(n < 200) { a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' ')) 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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' ')) 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,signif(numsignificant1,6)) a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' ')) 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,signif(numsignificant5,6)) a<-table.element(a,signif(numsignificant5/numgqtests,6)) 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,signif(numsignificant10,6)) a<-table.element(a,signif(numsignificant10/numgqtests,6)) 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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