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Data X:
'12.9' 2011 1 11 8 7 18 12 20 4 0 21 0 299639 149 '12.2' 2011 1 19 18 20 23 20 19 4 1 26 139 279529 139 '12.8' 2011 1 16 12 9 22 14 18 5 0 22 0 297628 148 '7.4' 2011 1 24 24 19 22 25 24 4 1 22 158 317738 158 '6.7' 2011 1 15 16 12 19 15 20 4 1 18 128 257408 128 '12.6' 2011 1 17 19 16 25 20 20 9 1 23 224 450464 224 '14.8' 2011 1 19 16 17 28 21 24 8 0 12 0 319749 159 '13.3' 2011 1 19 15 9 16 15 21 11 1 20 105 211155 105 '11.1' 2011 1 28 28 28 28 28 28 4 1 22 159 319749 159 '8.2' 2011 1 26 21 20 21 11 10 4 1 21 167 335837 167 '11.4' 2011 1 15 18 16 22 22 22 6 1 19 165 331815 165 '6.4' 2011 1 26 22 22 24 22 19 4 1 22 159 319749 159 '10.6' 2011 1 16 19 17 24 27 27 8 1 15 119 239309 119 12 2011 1 24 22 12 26 24 23 4 0 20 0 353936 176 '6.3' 2011 1 25 25 18 28 23 24 4 0 19 0 108594 54 '11.3' 2011 0 22 20 20 24 24 24 11 0 18 0 183001 0 '11.9' 2011 1 15 16 12 20 21 25 4 1 15 163 327793 163 '9.3' 2011 1 21 19 16 26 20 24 4 0 20 0 249364 124 '9.6' 2011 0 22 18 16 21 19 21 6 1 21 137 275507 0 10 2011 1 27 26 21 28 25 28 6 0 21 0 243331 121 '6.4' 2011 1 26 24 15 27 16 28 4 1 15 153 307683 153 '13.8' 2011 1 26 20 17 23 24 22 8 1 16 148 297628 148 '10.8' 2011 1 22 19 17 24 21 26 5 0 23 0 444431 221 '13.8' 2011 1 21 19 17 24 22 26 4 1 21 188 378068 188 '11.7' 2011 1 22 23 18 22 25 21 9 1 18 149 299639 149 '10.9' 2011 1 20 18 15 21 23 26 4 1 25 244 490684 244 '16.1' 2011 0 21 16 20 25 20 23 7 1 9 148 297628 0 '13.4' 2011 0 20 18 13 20 21 20 10 0 30 0 185012 0 '9.9' 2011 1 22 21 21 21 22 24 4 1 20 150 301650 150 '11.5' 2011 1 21 20 12 26 25 25 4 0 23 0 307683 153 '8.3' 2011 1 8 15 6 23 23 24 7 0 16 0 189034 94 '11.7' 2011 1 22 19 13 21 19 20 12 0 16 0 313716 156 9 2011 1 20 19 19 27 21 24 7 1 19 132 265452 132 '9.7' 2011 1 24 7 12 25 19 25 5 1 25 161 323771 161 '10.8' 2011 1 17 20 14 23 25 23 8 1 25 105 211155 105 '10.3' 2011 1 20 20 13 25 16 21 5 1 18 97 195067 97 '10.4' 2011 1 23 19 12 23 24 23 4 0 23 0 303661 151 '12.7' 2011 0 20 19 17 19 24 21 9 1 21 131 263441 0 '9.3' 2011 1 22 20 19 22 18 18 7 1 10 166 333826 166 '11.8' 2011 1 19 18 10 24 28 24 4 0 14 0 315727 157 '5.9' 2011 1 15 14 10 19 15 18 4 1 22 111 223221 111 '11.4' 2011 1 20 17 11 21 17 21 4 1 26 145 291595 145 13 2011 1 22 17 11 27 18 23 4 1 23 162 325782 162 '10.8' 2011 1 17 8 10 25 26 25 4 1 23 163 327793 163 '12.3' 2011 0 14 9 7 25 18 22 7 1 24 59 118649 0 '11.3' 2011 1 24 22 22 23 22 22 4 0 24 0 376057 187 '11.8' 2011 1 17 20 12 17 19 23 7 1 18 109 219199 109 '7.9' 2011 0 23 20 18 28 17 24 4 1 23 90 180990 0 '12.7' 2011 1 25 22 20 25 26 25 4 0 15 0 211155 105 '12.3' 2011 0 16 22 9 20 21 22 4 1 19 83 166913 0 '11.6' 2011 0 18 22 16 25 26 24 4 1 16 116 233276 0 '6.7' 2011 0 20 16 14 21 21 21 8 1 25 42 84462 0 '10.9' 2011 1 18 14 11 24 12 24 4 1 23 148 297628 148 '12.1' 2011 0 23 24 20 28 20 25 4 1 17 155 311705 0 '13.3' 2011 1 24 21 17 20 20 23 4 1 19 125 251375 125 '10.1' 2011 1 23 20 14 19 24 27 4 1 21 116 233276 116 '5.7' 2011 0 13 20 8 24 24 27 7 0 18 0 257408 0 '14.3' 2011 1 20 18 16 21 22 23 12 1 27 138 277518 138 8 2011 0 20 14 11 24 21 18 4 0 21 0 98539 0 '13.3' 2011 0 19 19 10 23 20 20 4 1 13 96 193056 0 '9.3' 2011 1 22 24 15 18 23 23 4 1 8 164 329804 164 '12.5' 2011 1 22 19 15 27 19 24 5 0 29 0 325782 162 '7.6' 2011 1 15 16 10 25 24 26 15 0 28 0 199089 99 '15.9' 2011 1 17 16 10 20 21 20 5 1 23 202 406222 202 '9.2' 2011 1 19 16 18 21 16 23 10 0 21 0 374046 186 '9.1' 2011 0 20 14 10 23 17 22 9 1 19 66 132726 0 '11.1' 2011 1 22 22 22 27 23 23 8 0 19 0 368013 183 13 2011 1 21 21 16 24 20 17 4 1 20 214 430354 214 '14.5' 2011 1 21 15 10 27 19 20 5 1 18 188 378068 188 '12.2' 2011 0 16 14 7 24 18 22 4 0 19 0 209144 0 '12.3' 2011 1 20 15 16 23 18 18 9 0 17 0 355947 177 '11.4' 2011 1 21 14 16 24 21 19 4 0 19 0 253386 126 '8.8' 2011 0 20 20 16 21 20 19 10 0 25 0 152836 0 '14.6' 2011 0 23 21 22 23 17 16 4 1 19 99 199089 0 '12.6' 2011 1 18 14 5 27 25 26 4 0 22 0 279529 139 NA 2011 1 22 19 18 24 15 14 6 1 23 78 156858 78 13 2011 1 16 16 10 25 17 25 7 0 26 0 325782 162 '12.6' 2011 0 17 13 8 19 17 23 5 1 14 108 217188 0 '13.2' 2011 1 24 26 16 24 24 18 4 0 28 0 319749 159 '9.9' 2011 0 13 13 8 25 21 22 4 0 16 0 148814 0 '7.7' 2011 1 19 18 16 23 22 26 4 1 24 110 221210 110 '10.5' 2011 0 20 15 14 23 18 25 4 0 20 0 193056 0 '13.4' 2011 0 22 18 15 25 22 26 4 0 12 0 233276 0 '10.9' 2011 0 19 21 9 26 20 26 4 0 24 0 174957 0 '4.3' 2011 0 21 17 21 26 21 24 6 1 22 97 195067 0 '10.3' 2011 0 15 18 7 16 21 22 10 0 12 0 255397 0 '11.8' 2011 0 21 20 17 23 20 21 7 1 22 106 213166 0 '11.2' 2011 0 24 18 18 26 18 22 4 1 20 80 160880 0 '11.4' 2011 0 22 25 16 25 25 28 4 0 10 0 148814 0 '8.6' 2011 0 20 20 16 23 23 22 7 0 23 0 183001 0 '13.2' 2011 0 21 19 14 26 21 26 4 0 17 0 267463 0 '12.6' 2011 0 19 18 15 22 20 20 8 1 22 74 148814 0 '5.6' 2011 0 14 12 8 20 21 24 11 1 24 114 229254 0 '9.9' 2011 0 25 22 22 27 20 21 6 1 18 140 281540 0 '8.8' 2011 0 11 16 5 20 22 23 14 0 21 0 191045 0 '7.7' 2011 0 17 18 13 22 15 23 5 1 20 98 197078 0 9 2011 0 22 23 22 24 24 23 4 0 20 0 243331 0 '7.3' 2011 0 20 20 18 21 22 22 8 1 22 126 253386 0 '11.4' 2011 0 22 20 15 24 21 23 9 1 19 98 197078 0 '13.6' 2011 0 15 16 11 26 17 21 4 1 20 95 191045 0 '7.9' 2011 0 23 22 19 24 23 27 4 1 26 110 221210 0 '10.7' 2011 0 20 19 19 24 22 23 5 1 23 70 140770 0 '10.3' 2011 0 22 23 21 27 23 26 4 0 24 0 205122 0 '8.3' 2011 0 16 6 4 25 16 27 5 1 21 86 172946 0 '9.6' 2011 0 25 19 17 27 18 27 4 1 21 130 261430 0 '14.2' 2011 0 18 24 10 19 25 23 4 1 19 96 193056 0 '8.5' 2011 0 19 19 13 22 18 23 7 0 8 0 205122 0 '13.5' 2011 0 25 15 15 22 14 23 10 0 17 0 201100 0 '4.9' 2011 0 21 18 11 25 20 28 4 0 20 0 189034 0 '6.4' 2011 0 22 18 20 23 19 24 5 0 11 0 104572 0 '9.6' 2011 0 21 22 13 24 18 20 4 0 8 0 197078 0 '11.6' 2011 0 22 23 18 24 22 23 4 0 15 0 237298 0 '11.1' 2011 0 23 18 20 23 21 22 4 1 18 99 199089 0 '4.35' 2012 1 20 17 15 22 14 15 6 1 18 48 96576 48 '12.7' 2012 1 6 6 4 24 5 27 4 1 19 50 100600 50 '18.1' 2012 1 15 22 9 19 25 23 8 1 19 150 301800 150 '17.85' 2012 1 18 20 18 25 21 23 5 1 23 154 309848 154 '16.6' 2012 0 24 16 12 26 11 20 4 0 22 0 219308 0 '12.6' 2012 0 22 16 17 18 20 18 17 1 21 68 136816 0 '17.1' 2012 1 21 17 12 24 9 22 4 1 25 194 390328 194 '19.1' 2012 1 23 20 16 28 15 20 4 0 30 0 317896 158 '16.1' 2012 1 20 23 17 23 23 21 8 1 17 159 319908 159 '13.35' 2012 1 20 18 14 19 21 25 4 0 27 0 134804 67 '18.4' 2012 1 18 13 13 19 9 19 7 0 23 0 295764 147 '14.7' 2012 1 25 22 20 27 24 25 4 1 23 39 78468 39 '10.6' 2012 1 16 20 16 24 16 24 4 1 18 100 201200 100 '12.6' 2012 1 20 20 15 26 20 22 5 1 18 111 223332 111 '16.2' 2012 1 14 13 10 21 15 28 7 1 23 138 277656 138 '13.6' 2012 1 22 16 16 25 18 22 4 1 19 101 203212 101 '18.9' 2012 0 26 25 21 28 22 21 4 1 15 131 263572 0 '14.1' 2012 1 20 16 15 19 21 23 7 1 20 101 203212 101 '14.5' 2012 1 17 15 16 20 21 19 11 1 16 114 229368 114 '16.15' 2012 1 22 19 19 26 21 21 7 0 24 0 331980 165 '14.75' 2012 1 22 19 9 27 20 25 4 1 25 114 229368 114 '14.8' 2012 1 20 24 19 23 24 23 4 1 25 111 223332 111 '12.45' 2012 1 17 9 7 18 15 28 4 1 19 75 150900 75 '12.65' 2012 1 22 22 23 23 24 14 4 1 19 82 164984 82 '17.35' 2012 1 17 15 14 21 18 23 4 1 16 121 243452 121 '8.6' 2012 1 22 22 10 23 24 24 4 1 19 32 64384 32 '18.4' 2012 1 21 22 16 22 24 25 6 0 19 0 301800 150 '16.1' 2012 1 25 24 12 21 15 15 8 1 23 117 235404 117 '11.6' 2012 0 11 12 10 14 19 23 23 1 21 71 142852 0 '17.75' 2012 1 19 21 7 24 20 26 4 1 22 165 331980 165 '15.25' 2012 1 24 25 20 26 26 21 8 1 19 154 309848 154 '17.65' 2012 1 17 26 9 24 26 26 6 1 20 126 253512 126 '16.35' 2012 1 22 21 12 22 23 23 4 0 20 0 299788 149 '17.65' 2012 1 17 14 10 20 13 15 7 0 3 0 291740 145 '13.6' 2012 1 26 28 19 20 16 16 4 1 23 120 241440 120 '14.35' 2012 1 20 21 11 18 22 20 4 0 14 0 219308 109 '14.75' 2012 1 19 16 15 18 21 20 4 0 23 0 265584 132 '18.25' 2012 1 21 16 14 25 11 21 10 1 20 172 346064 172 '9.9' 2012 1 24 25 11 28 23 28 6 0 15 0 340028 169 16 2012 1 21 21 14 23 18 19 5 1 13 114 229368 114 '18.25' 2012 1 19 22 15 20 19 21 5 1 16 156 313872 156 '16.85' 2012 1 13 9 7 22 15 22 4 0 7 0 346064 172 '14.6' 2012 0 24 20 22 27 8 27 4 1 24 68 136816 0 '13.85' 2012 0 28 19 19 24 15 20 5 1 17 89 179068 0 '18.95' 2012 1 27 24 22 23 21 17 5 1 24 167 336004 167 '15.6' 2012 1 22 22 11 20 25 26 5 0 24 0 227356 113 '14.85' 2012 0 23 22 19 22 14 21 5 0 19 0 231380 0 '11.75' 2012 0 19 12 9 21 21 24 4 0 25 0 156936 0 '18.45' 2012 0 18 17 11 24 18 21 6 0 20 0 237416 0 '15.9' 2012 0 23 18 17 26 18 25 4 1 28 87 175044 0 '17.1' 2012 1 21 10 12 24 12 22 4 0 23 0 348076 173 '16.1' 2012 1 22 22 17 18 24 17 4 1 27 2 4024 2 '19.9' 2012 0 17 24 10 17 17 14 9 0 18 0 325944 0 '10.95' 2012 0 15 18 17 23 20 23 18 1 28 49 98588 0 '18.45' 2012 0 21 18 13 21 24 28 6 0 21 0 245464 0 '15.1' 2012 0 20 23 11 21 22 24 5 1 19 96 193152 0 15 2012 0 26 21 19 24 15 22 4 0 23 0 201200 0 '11.35' 2012 0 19 21 21 22 22 24 11 0 27 0 164984 0 '15.95' 2012 0 28 28 24 24 26 25 4 1 22 100 201200 0 '18.1' 2012 0 21 17 13 24 17 21 10 0 28 0 231380 0 '14.6' 2012 0 19 21 16 24 23 22 6 1 25 141 283692 0 '15.4' 2012 1 22 21 13 23 19 16 8 1 21 165 331980 165 '15.4' 2012 1 21 20 15 21 21 18 8 1 22 165 331980 165 '17.6' 2012 0 20 18 15 24 23 27 6 1 28 110 221320 0 '13.35' 2012 1 19 17 11 19 19 17 8 1 20 118 237416 118 '19.1' 2012 1 11 7 7 19 18 25 4 0 29 0 317896 158 '15.35' 2012 0 17 17 13 23 16 24 4 1 25 146 293752 0 '7.6' 2012 1 19 14 13 25 23 21 9 0 25 0 98588 49 '13.4' 2012 0 20 18 12 24 13 21 9 0 20 0 181080 0 '13.9' 2012 0 17 14 8 21 18 19 5 0 20 0 243452 0 '19.1' 2012 1 21 23 7 18 23 27 4 1 16 155 311860 155 '15.25' 2012 0 21 20 17 23 21 28 4 0 20 0 209248 0 '12.9' 2012 0 12 14 9 20 23 19 15 1 20 147 295764 0 '16.1' 2012 0 23 17 18 23 16 23 10 0 23 0 221320 0 '17.35' 2012 0 22 21 17 23 17 25 9 0 18 0 217296 0 '13.15' 2012 0 22 23 17 23 20 26 7 0 25 0 227356 0 '12.15' 2012 0 21 24 18 23 18 25 9 0 18 0 231380 0 '12.6' 2012 0 20 21 12 27 20 25 6 1 19 61 122732 0 '10.35' 2012 0 18 14 14 19 19 24 4 1 25 60 120720 0 '15.4' 2012 0 21 24 22 25 26 24 7 1 25 109 219308 0 '9.6' 2012 0 24 16 19 25 9 24 4 1 25 68 136816 0 '18.2' 2012 0 22 21 21 21 23 22 7 0 24 0 223332 0 '13.6' 2012 0 20 8 10 25 9 21 4 0 19 0 154924 0 '14.85' 2012 0 17 17 16 17 13 17 15 1 26 73 146876 0 '14.75' 2012 1 19 18 11 22 27 23 4 0 10 0 303812 151 '14.1' 2012 0 16 17 15 23 22 17 9 0 17 0 179068 0 '14.9' 2012 0 19 16 12 27 12 25 4 0 13 0 156936 0 '16.25' 2012 0 23 22 21 27 18 19 4 0 17 0 221320 0 '19.25' 2012 1 8 17 22 5 6 8 28 1 30 220 442640 220 '13.6' 2012 0 22 21 20 19 17 14 4 1 25 65 130780 0 '13.6' 2012 1 23 20 15 24 22 22 4 0 4 0 283692 141 '15.65' 2012 0 15 20 9 23 22 25 4 0 16 0 235404 0 '12.75' 2012 1 17 19 15 28 23 28 5 1 21 122 245464 122 '14.6' 2012 0 21 8 14 25 19 25 4 0 23 0 126756 0 '9.85' 2012 1 25 19 11 27 20 24 4 1 22 44 88528 44 '12.65' 2012 0 18 11 9 16 17 15 12 1 17 52 104624 0 '19.2' 2012 0 20 13 12 25 24 24 4 0 20 0 263572 0 '16.6' 2012 0 21 18 11 26 20 28 6 1 20 101 203212 0 '11.2' 2012 0 21 19 14 24 18 24 6 1 22 42 84504 0 '15.25' 2012 1 24 23 10 23 23 25 5 1 16 152 305824 152 '11.9' 2012 1 22 20 18 24 27 23 4 0 23 0 215284 107 '13.2' 2012 0 22 22 11 27 25 26 4 0 16 0 154924 0 '16.35' 2012 1 23 19 14 25 24 26 4 0 0 0 309848 154 '12.4' 2012 1 17 16 16 19 12 22 10 1 18 103 207236 103 '15.85' 2012 0 15 11 11 19 16 25 7 1 25 96 193152 0 '18.15' 2012 1 22 21 16 24 24 22 4 1 23 175 352100 175 '11.15' 2012 0 19 14 13 20 23 26 7 1 12 57 114684 0 '15.65' 2012 0 18 21 12 21 24 20 4 0 18 0 225344 0 '17.75' 2012 1 21 20 17 28 24 26 4 0 24 0 287716 143 '7.65' 2012 0 20 21 23 26 26 26 12 0 11 0 98588 0 '12.35' 2012 1 19 20 14 19 19 21 5 1 18 110 221320 110 '15.6' 2012 1 19 19 10 23 28 21 8 1 14 131 263572 131 '19.3' 2012 1 16 19 16 23 23 24 6 0 23 0 336004 167 '15.2' 2012 0 18 18 11 21 21 21 17 0 24 0 112672 0 '17.1' 2012 1 23 20 16 26 19 18 4 0 29 0 275644 137 '15.6' 2012 0 22 21 19 25 23 23 5 1 18 86 173032 0 '18.4' 2012 1 23 22 17 25 23 26 4 1 15 121 243452 121 '19.05' 2012 1 20 19 12 24 20 23 5 0 29 0 299788 149 '18.55' 2012 1 24 23 17 23 18 25 5 0 16 0 338016 168 '19.1' 2012 1 25 16 11 22 20 20 6 0 19 0 281680 140 '13.1' 2012 0 25 23 19 27 28 25 4 1 22 88 177056 0 '12.85' 2012 1 20 18 12 26 21 26 4 1 16 168 338016 168 '9.5' 2012 1 23 23 8 23 25 19 4 1 23 94 189128 94 '4.5' 2012 1 21 20 17 22 18 21 6 1 23 51 102612 51 '11.85' 2012 0 23 20 13 26 24 23 8 0 19 0 96576 0 '13.6' 2012 1 23 23 17 22 28 24 10 1 4 145 291740 145 '11.7' 2012 1 11 13 7 17 9 6 4 1 20 66 132792 66 '12.4' 2012 0 21 21 23 25 22 22 5 1 24 85 171020 0 '13.35' 2012 1 27 26 18 22 26 21 4 0 20 0 219308 109 '11.4' 2012 0 19 18 13 28 28 28 4 0 4 0 126756 0 '14.9' 2012 0 21 19 17 22 18 24 4 1 24 102 205224 0 '19.9' 2012 0 16 18 13 21 23 14 16 0 22 0 325944 0 '11.2' 2012 0 21 18 8 24 15 20 7 1 16 86 173032 0 '14.6' 2012 0 22 19 16 26 24 28 4 1 3 114 229368 0 '17.6' 2012 1 16 13 14 26 12 19 4 0 15 0 329968 164 '14.05' 2012 1 18 10 13 24 12 24 14 1 24 119 239428 119 '16.1' 2012 1 23 21 19 27 20 21 5 0 17 0 253512 126 '13.35' 2012 1 24 24 15 22 25 21 5 1 20 132 265584 132 '11.85' 2012 1 20 21 15 23 24 26 5 1 27 142 285704 142 '11.95' 2012 1 20 23 8 22 23 24 5 0 23 0 166996 83 '14.75' 2012 0 18 18 14 23 18 26 7 1 26 94 189128 0 '15.15' 2012 0 4 11 7 15 20 25 19 0 23 0 162972 0 '13.2' 2012 1 14 16 11 20 22 23 16 1 17 166 333992 166 '16.85' 2012 0 22 20 17 22 20 24 4 0 20 0 221320 0 '7.85' 2012 0 17 20 19 25 25 24 4 1 22 64 128768 0 '7.7' 2012 1 23 26 17 27 28 26 7 0 19 0 187116 93 '12.6' 2012 0 20 21 12 24 25 23 9 0 24 0 209248 0 '7.85' 2012 0 18 12 12 21 14 20 5 1 19 105 211260 0 '10.95' 2012 0 19 15 18 17 16 16 14 1 23 49 98588 0 '12.35' 2012 0 20 18 16 26 24 24 4 0 15 0 177056 0 '9.95' 2012 0 15 14 15 20 13 20 16 1 27 95 191140 0 '14.9' 2012 0 24 18 20 22 19 23 10 1 26 102 205224 0 '16.65' 2012 0 21 16 16 24 18 23 5 0 22 0 199188 0 '13.4' 2012 0 19 19 12 23 16 18 6 1 22 63 126756 0 '13.95' 2012 0 19 7 10 22 8 21 4 0 18 0 152912 0 '15.7' 2012 0 27 21 28 28 27 25 4 0 15 0 219308 0 '16.85' 2012 0 23 24 19 21 23 23 4 1 22 117 235404 0 '10.95' 2012 0 23 21 18 24 20 26 5 1 27 57 114684 0 '15.35' 2012 0 20 20 19 28 20 26 4 0 10 0 241440 0 '12.2' 2012 0 17 22 8 25 26 24 4 1 20 73 146876 0 '15.1' 2012 0 21 17 17 24 23 23 5 0 17 0 183092 0 '17.75' 2012 0 23 19 16 24 24 21 4 0 23 0 217296 0 '15.2' 2012 0 22 20 18 21 21 23 4 1 19 105 211260 0 '14.6' 2012 1 16 16 12 20 15 20 5 0 13 0 235404 117 '16.65' 2012 0 20 20 17 26 22 23 8 0 27 0 239428 0 '8.1' 2012 0 16 16 13 16 25 24 15 1 23 31 62372 0
Names of X columns:
TOT Jaar GroepN AMS.I1 AMS.I2 AMS.I3 AMS.E1 AMS.E2 AMS.E3 AMS.A gender NUMERACYTOT_ Gender*LFM Jaar*LFM Groep*LFM
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,signif(mysum$coefficients[i,1],6)) a<-table.element(a, signif(mysum$coefficients[i,2],6)) a<-table.element(a, signif(mysum$coefficients[i,3],4)) a<-table.element(a, signif(mysum$coefficients[i,4],6)) a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, signif(sqrt(mysum$r.squared),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, signif(mysum$r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, signif(mysum$adj.r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[1],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$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$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) 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, signif(mysum$sigma,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, signif(sum(myerror*myerror),6)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,signif(x[i],6)) a<-table.element(a,signif(x[i]-mysum$resid[i],6)) a<-table.element(a,signif(mysum$resid[i],6)) 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,signif(gqarr[mypoint-kp3+1,1],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) 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,signif(numsignificant1/numgqtests,6)) 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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