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Data X:
12.9 0 1 0 11 8 7 18 12 20 4 21 149 68 0 149 0 12.2 0 1 1 19 18 20 23 20 19 4 22 139 39 139 139 0 12.8 0 1 0 16 12 9 22 14 18 5 22 148 32 0 148 0 7.4 0 1 1 24 24 19 22 25 24 4 18 158 62 158 158 0 6.7 0 1 1 15 16 12 19 15 20 4 23 128 33 128 128 0 12.6 0 1 1 17 19 16 25 20 20 9 12 224 52 224 224 0 14.8 0 1 0 19 16 17 28 21 24 8 20 159 62 0 159 0 13.3 0 1 1 19 15 9 16 15 21 11 22 105 77 105 105 0 11.1 0 1 1 28 28 28 28 28 28 4 21 159 76 159 159 0 8.2 0 1 1 26 21 20 21 11 10 4 19 167 41 167 167 0 11.4 0 1 1 15 18 16 22 22 22 6 22 165 48 165 165 0 6.4 0 1 1 26 22 22 24 22 19 4 15 159 63 159 159 0 10.6 0 1 1 16 19 17 24 27 27 8 20 119 30 119 119 0 12 0 1 0 24 22 12 26 24 23 4 19 176 78 0 176 0 6.3 0 1 0 25 25 18 28 23 24 4 18 54 19 0 54 0 11.3 0 0 0 22 20 20 24 24 24 11 15 91 31 0 0 0 11.9 0 1 1 15 16 12 20 21 25 4 20 163 66 163 163 0 9.3 0 1 0 21 19 16 26 20 24 4 21 124 35 0 124 0 9.6 0 0 1 22 18 16 21 19 21 6 21 137 42 137 0 0 10 0 1 0 27 26 21 28 25 28 6 15 121 45 0 121 0 6.4 0 1 1 26 24 15 27 16 28 4 16 153 21 153 153 0 13.8 0 1 1 26 20 17 23 24 22 8 23 148 25 148 148 0 10.8 0 1 0 22 19 17 24 21 26 5 21 221 44 0 221 0 13.8 0 1 1 21 19 17 24 22 26 4 18 188 69 188 188 0 11.7 0 1 1 22 23 18 22 25 21 9 25 149 54 149 149 0 10.9 0 1 1 20 18 15 21 23 26 4 9 244 74 244 244 0 16.1 0 0 1 21 16 20 25 20 23 7 30 148 80 148 0 0 13.4 0 0 0 20 18 13 20 21 20 10 20 92 42 0 0 0 9.9 0 1 1 22 21 21 21 22 24 4 23 150 61 150 150 0 11.5 0 1 0 21 20 12 26 25 25 4 16 153 41 0 153 0 8.3 0 1 0 8 15 6 23 23 24 7 16 94 46 0 94 0 11.7 0 1 0 22 19 13 21 19 20 12 19 156 39 0 156 0 9 0 1 1 20 19 19 27 21 24 7 25 132 34 132 132 0 9.7 0 1 1 24 7 12 25 19 25 5 18 161 51 161 161 0 10.8 0 1 1 17 20 14 23 25 23 8 23 105 42 105 105 0 10.3 0 1 1 20 20 13 25 16 21 5 21 97 31 97 97 0 10.4 0 1 0 23 19 12 23 24 23 4 10 151 39 0 151 0 12.7 0 0 1 20 19 17 19 24 21 9 14 131 20 131 0 0 9.3 0 1 1 22 20 19 22 18 18 7 22 166 49 166 166 0 11.8 0 1 0 19 18 10 24 28 24 4 26 157 53 0 157 0 5.9 0 1 1 15 14 10 19 15 18 4 23 111 31 111 111 0 11.4 0 1 1 20 17 11 21 17 21 4 23 145 39 145 145 0 13 0 1 1 22 17 11 27 18 23 4 24 162 54 162 162 0 10.8 0 1 1 17 8 10 25 26 25 4 24 163 49 163 163 0 12.3 0 0 1 14 9 7 25 18 22 7 18 59 34 59 0 0 11.3 0 1 0 24 22 22 23 22 22 4 23 187 46 0 187 0 11.8 0 1 1 17 20 12 17 19 23 7 15 109 55 109 109 0 7.9 0 0 1 23 20 18 28 17 24 4 19 90 42 90 0 0 12.7 0 1 0 25 22 20 25 26 25 4 16 105 50 0 105 0 12.3 0 0 1 16 22 9 20 21 22 4 25 83 13 83 0 0 11.6 0 0 1 18 22 16 25 26 24 4 23 116 37 116 0 0 6.7 0 0 1 20 16 14 21 21 21 8 17 42 25 42 0 0 10.9 0 1 1 18 14 11 24 12 24 4 19 148 30 148 148 0 12.1 0 0 1 23 24 20 28 20 25 4 21 155 28 155 0 0 13.3 0 1 1 24 21 17 20 20 23 4 18 125 45 125 125 0 10.1 0 1 1 23 20 14 19 24 27 4 27 116 35 116 116 0 5.7 0 0 0 13 20 8 24 24 27 7 21 128 28 0 0 0 14.3 0 1 1 20 18 16 21 22 23 12 13 138 41 138 138 0 8 0 0 0 20 14 11 24 21 18 4 8 49 6 0 0 0 13.3 0 0 1 19 19 10 23 20 20 4 29 96 45 96 0 0 9.3 0 1 1 22 24 15 18 23 23 4 28 164 73 164 164 0 12.5 0 1 0 22 19 15 27 19 24 5 23 162 17 0 162 0 7.6 0 1 0 15 16 10 25 24 26 15 21 99 40 0 99 0 15.9 0 1 1 17 16 10 20 21 20 5 19 202 64 202 202 0 9.2 0 1 0 19 16 18 21 16 23 10 19 186 37 0 186 0 9.1 0 0 1 20 14 10 23 17 22 9 20 66 25 66 0 0 11.1 0 1 0 22 22 22 27 23 23 8 18 183 65 0 183 0 13 0 1 1 21 21 16 24 20 17 4 19 214 100 214 214 0 14.5 0 1 1 21 15 10 27 19 20 5 17 188 28 188 188 0 12.2 0 0 0 16 14 7 24 18 22 4 19 104 35 0 0 0 12.3 0 1 0 20 15 16 23 18 18 9 25 177 56 0 177 0 11.4 0 1 0 21 14 16 24 21 19 4 19 126 29 0 126 0 8.8 0 0 0 20 20 16 21 20 19 10 22 76 43 0 0 0 14.6 0 0 1 23 21 22 23 17 16 4 23 99 59 99 0 0 12.6 0 1 0 18 14 5 27 25 26 4 14 139 50 0 139 0 13 0 1 0 16 16 10 25 17 25 7 16 162 59 0 162 0 12.6 0 0 1 17 13 8 19 17 23 5 24 108 27 108 0 0 13.2 0 1 0 24 26 16 24 24 18 4 20 159 61 0 159 0 9.9 0 0 0 13 13 8 25 21 22 4 12 74 28 0 0 0 7.7 0 1 1 19 18 16 23 22 26 4 24 110 51 110 110 0 10.5 0 0 0 20 15 14 23 18 25 4 22 96 35 0 0 0 13.4 0 0 0 22 18 15 25 22 26 4 12 116 29 0 0 0 10.9 0 0 0 19 21 9 26 20 26 4 22 87 48 0 0 0 4.3 0 0 1 21 17 21 26 21 24 6 20 97 25 97 0 0 10.3 0 0 0 15 18 7 16 21 22 10 10 127 44 0 0 0 11.8 0 0 1 21 20 17 23 20 21 7 23 106 64 106 0 0 11.2 0 0 1 24 18 18 26 18 22 4 17 80 32 80 0 0 11.4 0 0 0 22 25 16 25 25 28 4 22 74 20 0 0 0 8.6 0 0 0 20 20 16 23 23 22 7 24 91 28 0 0 0 13.2 0 0 0 21 19 14 26 21 26 4 18 133 34 0 0 0 12.6 0 0 1 19 18 15 22 20 20 8 21 74 31 74 0 0 5.6 0 0 1 14 12 8 20 21 24 11 20 114 26 114 0 0 9.9 0 0 1 25 22 22 27 20 21 6 20 140 58 140 0 0 8.8 0 0 0 11 16 5 20 22 23 14 22 95 23 0 0 0 7.7 0 0 1 17 18 13 22 15 23 5 19 98 21 98 0 0 9 0 0 0 22 23 22 24 24 23 4 20 121 21 0 0 0 7.3 0 0 1 20 20 18 21 22 22 8 26 126 33 126 0 0 11.4 0 0 1 22 20 15 24 21 23 9 23 98 16 98 0 0 13.6 0 0 1 15 16 11 26 17 21 4 24 95 20 95 0 0 7.9 0 0 1 23 22 19 24 23 27 4 21 110 37 110 0 0 10.7 0 0 1 20 19 19 24 22 23 5 21 70 35 70 0 0 10.3 0 0 0 22 23 21 27 23 26 4 19 102 33 0 0 0 8.3 0 0 1 16 6 4 25 16 27 5 8 86 27 86 0 0 9.6 0 0 1 25 19 17 27 18 27 4 17 130 41 130 0 0 14.2 0 0 1 18 24 10 19 25 23 4 20 96 40 96 0 0 8.5 0 0 0 19 19 13 22 18 23 7 11 102 35 0 0 0 13.5 0 0 0 25 15 15 22 14 23 10 8 100 28 0 0 0 4.9 0 0 0 21 18 11 25 20 28 4 15 94 32 0 0 0 6.4 0 0 0 22 18 20 23 19 24 5 18 52 22 0 0 0 9.6 0 0 0 21 22 13 24 18 20 4 18 98 44 0 0 0 11.6 0 0 0 22 23 18 24 22 23 4 19 118 27 0 0 0 11.1 0 0 1 23 18 20 23 21 22 4 19 99 17 99 0 0 4.35 1 1 1 20 17 15 22 14 15 6 23 48 12 48 48 48 12.7 1 1 1 6 6 4 24 5 27 4 22 50 45 50 50 50 18.1 1 1 1 15 22 9 19 25 23 8 21 150 37 150 150 150 17.85 1 1 1 18 20 18 25 21 23 5 25 154 37 154 154 154 16.6 1 0 0 24 16 12 26 11 20 4 30 109 108 0 0 109 12.6 1 0 1 22 16 17 18 20 18 17 17 68 10 68 0 68 17.1 1 1 1 21 17 12 24 9 22 4 27 194 68 194 194 194 19.1 1 1 0 23 20 16 28 15 20 4 23 158 72 0 158 158 16.1 1 1 1 20 23 17 23 23 21 8 23 159 143 159 159 159 13.35 1 1 0 20 18 14 19 21 25 4 18 67 9 0 67 67 18.4 1 1 0 18 13 13 19 9 19 7 18 147 55 0 147 147 14.7 1 1 1 25 22 20 27 24 25 4 23 39 17 39 39 39 10.6 1 1 1 16 20 16 24 16 24 4 19 100 37 100 100 100 12.6 1 1 1 20 20 15 26 20 22 5 15 111 27 111 111 111 16.2 1 1 1 14 13 10 21 15 28 7 20 138 37 138 138 138 13.6 1 1 1 22 16 16 25 18 22 4 16 101 58 101 101 101 18.9 1 0 1 26 25 21 28 22 21 4 24 131 66 131 0 131 14.1 1 1 1 20 16 15 19 21 23 7 25 101 21 101 101 101 14.5 1 1 1 17 15 16 20 21 19 11 25 114 19 114 114 114 16.15 1 1 0 22 19 19 26 21 21 7 19 165 78 0 165 165 14.75 1 1 1 22 19 9 27 20 25 4 19 114 35 114 114 114 14.8 1 1 1 20 24 19 23 24 23 4 16 111 48 111 111 111 12.45 1 1 1 17 9 7 18 15 28 4 19 75 27 75 75 75 12.65 1 1 1 22 22 23 23 24 14 4 19 82 43 82 82 82 17.35 1 1 1 17 15 14 21 18 23 4 23 121 30 121 121 121 8.6 1 1 1 22 22 10 23 24 24 4 21 32 25 32 32 32 18.4 1 1 0 21 22 16 22 24 25 6 22 150 69 0 150 150 16.1 1 1 1 25 24 12 21 15 15 8 19 117 72 117 117 117 11.6 1 0 1 11 12 10 14 19 23 23 20 71 23 71 0 71 17.75 1 1 1 19 21 7 24 20 26 4 20 165 13 165 165 165 15.25 1 1 1 24 25 20 26 26 21 8 3 154 61 154 154 154 17.65 1 1 1 17 26 9 24 26 26 6 23 126 43 126 126 126 16.35 1 1 0 22 21 12 22 23 23 4 23 149 51 0 149 149 17.65 1 1 0 17 14 10 20 13 15 7 20 145 67 0 145 145 13.6 1 1 1 26 28 19 20 16 16 4 15 120 36 120 120 120 14.35 1 1 0 20 21 11 18 22 20 4 16 109 44 0 109 109 14.75 1 1 0 19 16 15 18 21 20 4 7 132 45 0 132 132 18.25 1 1 1 21 16 14 25 11 21 10 24 172 34 172 172 172 9.9 1 1 0 24 25 11 28 23 28 6 17 169 36 0 169 169 16 1 1 1 21 21 14 23 18 19 5 24 114 72 114 114 114 18.25 1 1 1 19 22 15 20 19 21 5 24 156 39 156 156 156 16.85 1 1 0 13 9 7 22 15 22 4 19 172 43 0 172 172 14.6 1 0 1 24 20 22 27 8 27 4 25 68 25 68 0 68 13.85 1 0 1 28 19 19 24 15 20 5 20 89 56 89 0 89 18.95 1 1 1 27 24 22 23 21 17 5 28 167 80 167 167 167 15.6 1 1 0 22 22 11 20 25 26 5 23 113 40 0 113 113 14.85 1 0 0 23 22 19 22 14 21 5 27 115 73 0 0 115 11.75 1 0 0 19 12 9 21 21 24 4 18 78 34 0 0 78 18.45 1 0 0 18 17 11 24 18 21 6 28 118 72 0 0 118 15.9 1 0 1 23 18 17 26 18 25 4 21 87 42 87 0 87 17.1 1 1 0 21 10 12 24 12 22 4 19 173 61 0 173 173 16.1 1 1 1 22 22 17 18 24 17 4 23 2 23 2 2 2 19.9 1 0 0 17 24 10 17 17 14 9 27 162 74 0 0 162 10.95 1 0 1 15 18 17 23 20 23 18 22 49 16 49 0 49 18.45 1 0 0 21 18 13 21 24 28 6 28 122 66 0 0 122 15.1 1 0 1 20 23 11 21 22 24 5 25 96 9 96 0 96 15 1 0 0 26 21 19 24 15 22 4 21 100 41 0 0 100 11.35 1 0 0 19 21 21 22 22 24 11 22 82 57 0 0 82 15.95 1 0 1 28 28 24 24 26 25 4 28 100 48 100 0 100 18.1 1 0 0 21 17 13 24 17 21 10 20 115 51 0 0 115 14.6 1 0 1 19 21 16 24 23 22 6 29 141 53 141 0 141 15.4 1 1 1 22 21 13 23 19 16 8 25 165 29 165 165 165 15.4 1 1 1 21 20 15 21 21 18 8 25 165 29 165 165 165 17.6 1 0 1 20 18 15 24 23 27 6 20 110 55 110 0 110 13.35 1 1 1 19 17 11 19 19 17 8 20 118 54 118 118 118 19.1 1 1 0 11 7 7 19 18 25 4 16 158 43 0 158 158 15.35 1 0 1 17 17 13 23 16 24 4 20 146 51 146 0 146 7.6 1 1 0 19 14 13 25 23 21 9 20 49 20 0 49 49 13.4 1 0 0 20 18 12 24 13 21 9 23 90 79 0 0 90 13.9 1 0 0 17 14 8 21 18 19 5 18 121 39 0 0 121 19.1 1 1 1 21 23 7 18 23 27 4 25 155 61 155 155 155 15.25 1 0 0 21 20 17 23 21 28 4 18 104 55 0 0 104 12.9 1 0 1 12 14 9 20 23 19 15 19 147 30 147 0 147 16.1 1 0 0 23 17 18 23 16 23 10 25 110 55 0 0 110 17.35 1 0 0 22 21 17 23 17 25 9 25 108 22 0 0 108 13.15 1 0 0 22 23 17 23 20 26 7 25 113 37 0 0 113 12.15 1 0 0 21 24 18 23 18 25 9 24 115 2 0 0 115 12.6 1 0 1 20 21 12 27 20 25 6 19 61 38 61 0 61 10.35 1 0 1 18 14 14 19 19 24 4 26 60 27 60 0 60 15.4 1 0 1 21 24 22 25 26 24 7 10 109 56 109 0 109 9.6 1 0 1 24 16 19 25 9 24 4 17 68 25 68 0 68 18.2 1 0 0 22 21 21 21 23 22 7 13 111 39 0 0 111 13.6 1 0 0 20 8 10 25 9 21 4 17 77 33 0 0 77 14.85 1 0 1 17 17 16 17 13 17 15 30 73 43 73 0 73 14.75 1 1 0 19 18 11 22 27 23 4 25 151 57 0 151 151 14.1 1 0 0 16 17 15 23 22 17 9 4 89 43 0 0 89 14.9 1 0 0 19 16 12 27 12 25 4 16 78 23 0 0 78 16.25 1 0 0 23 22 21 27 18 19 4 21 110 44 0 0 110 19.25 1 1 1 8 17 22 5 6 8 28 23 220 54 220 220 220 13.6 1 0 1 22 21 20 19 17 14 4 22 65 28 65 0 65 13.6 1 1 0 23 20 15 24 22 22 4 17 141 36 0 141 141 15.65 1 0 0 15 20 9 23 22 25 4 20 117 39 0 0 117 12.75 1 1 1 17 19 15 28 23 28 5 20 122 16 122 122 122 14.6 1 0 0 21 8 14 25 19 25 4 22 63 23 0 0 63 9.85 1 1 1 25 19 11 27 20 24 4 16 44 40 44 44 44 12.65 1 0 1 18 11 9 16 17 15 12 23 52 24 52 0 52 19.2 1 0 0 20 13 12 25 24 24 4 0 131 78 0 0 131 16.6 1 0 1 21 18 11 26 20 28 6 18 101 57 101 0 101 11.2 1 0 1 21 19 14 24 18 24 6 25 42 37 42 0 42 15.25 1 1 1 24 23 10 23 23 25 5 23 152 27 152 152 152 11.9 1 1 0 22 20 18 24 27 23 4 12 107 61 0 107 107 13.2 1 0 0 22 22 11 27 25 26 4 18 77 27 0 0 77 16.35 1 1 0 23 19 14 25 24 26 4 24 154 69 0 154 154 12.4 1 1 1 17 16 16 19 12 22 10 11 103 34 103 103 103 15.85 1 0 1 15 11 11 19 16 25 7 18 96 44 96 0 96 18.15 1 1 1 22 21 16 24 24 22 4 23 175 34 175 175 175 11.15 1 0 1 19 14 13 20 23 26 7 24 57 39 57 0 57 15.65 1 0 0 18 21 12 21 24 20 4 29 112 51 0 0 112 17.75 1 1 0 21 20 17 28 24 26 4 18 143 34 0 143 143 7.65 1 0 0 20 21 23 26 26 26 12 15 49 31 0 0 49 12.35 1 1 1 19 20 14 19 19 21 5 29 110 13 110 110 110 15.6 1 1 1 19 19 10 23 28 21 8 16 131 12 131 131 131 19.3 1 1 0 16 19 16 23 23 24 6 19 167 51 0 167 167 15.2 1 0 0 18 18 11 21 21 21 17 22 56 24 0 0 56 17.1 1 1 0 23 20 16 26 19 18 4 16 137 19 0 137 137 15.6 1 0 1 22 21 19 25 23 23 5 23 86 30 86 0 86 18.4 1 1 1 23 22 17 25 23 26 4 23 121 81 121 121 121 19.05 1 1 0 20 19 12 24 20 23 5 19 149 42 0 149 149 18.55 1 1 0 24 23 17 23 18 25 5 4 168 22 0 168 168 19.1 1 1 0 25 16 11 22 20 20 6 20 140 85 0 140 140 13.1 1 0 1 25 23 19 27 28 25 4 24 88 27 88 0 88 12.85 1 1 1 20 18 12 26 21 26 4 20 168 25 168 168 168 9.5 1 1 1 23 23 8 23 25 19 4 4 94 22 94 94 94 4.5 1 1 1 21 20 17 22 18 21 6 24 51 19 51 51 51 11.85 1 0 0 23 20 13 26 24 23 8 22 48 14 0 0 48 13.6 1 1 1 23 23 17 22 28 24 10 16 145 45 145 145 145 11.7 1 1 1 11 13 7 17 9 6 4 3 66 45 66 66 66 12.4 1 0 1 21 21 23 25 22 22 5 15 85 28 85 0 85 13.35 1 1 0 27 26 18 22 26 21 4 24 109 51 0 109 109 11.4 1 0 0 19 18 13 28 28 28 4 17 63 41 0 0 63 14.9 1 0 1 21 19 17 22 18 24 4 20 102 31 102 0 102 19.9 1 0 0 16 18 13 21 23 14 16 27 162 74 0 0 162 11.2 1 0 1 21 18 8 24 15 20 7 26 86 19 86 0 86 14.6 1 0 1 22 19 16 26 24 28 4 23 114 51 114 0 114 17.6 1 1 0 16 13 14 26 12 19 4 17 164 73 0 164 164 14.05 1 1 1 18 10 13 24 12 24 14 20 119 24 119 119 119 16.1 1 1 0 23 21 19 27 20 21 5 22 126 61 0 126 126 13.35 1 1 1 24 24 15 22 25 21 5 19 132 23 132 132 132 11.85 1 1 1 20 21 15 23 24 26 5 24 142 14 142 142 142 11.95 1 1 0 20 23 8 22 23 24 5 19 83 54 0 83 83 14.75 1 0 1 18 18 14 23 18 26 7 23 94 51 94 0 94 15.15 1 0 0 4 11 7 15 20 25 19 15 81 62 0 0 81 13.2 1 1 1 14 16 11 20 22 23 16 27 166 36 166 166 166 16.85 1 0 0 22 20 17 22 20 24 4 26 110 59 0 0 110 7.85 1 0 1 17 20 19 25 25 24 4 22 64 24 64 0 64 7.7 1 1 0 23 26 17 27 28 26 7 22 93 26 0 93 93 12.6 1 0 0 20 21 12 24 25 23 9 18 104 54 0 0 104 7.85 1 0 1 18 12 12 21 14 20 5 15 105 39 105 0 105 10.95 1 0 1 19 15 18 17 16 16 14 22 49 16 49 0 49 12.35 1 0 0 20 18 16 26 24 24 4 27 88 36 0 0 88 9.95 1 0 1 15 14 15 20 13 20 16 10 95 31 95 0 95 14.9 1 0 1 24 18 20 22 19 23 10 20 102 31 102 0 102 16.65 1 0 0 21 16 16 24 18 23 5 17 99 42 0 0 99 13.4 1 0 1 19 19 12 23 16 18 6 23 63 39 63 0 63 13.95 1 0 0 19 7 10 22 8 21 4 19 76 25 0 0 76 15.7 1 0 0 27 21 28 28 27 25 4 13 109 31 0 0 109 16.85 1 0 1 23 24 19 21 23 23 4 27 117 38 117 0 117 10.95 1 0 1 23 21 18 24 20 26 5 23 57 31 57 0 57 15.35 1 0 0 20 20 19 28 20 26 4 16 120 17 0 0 120 12.2 1 0 1 17 22 8 25 26 24 4 25 73 22 73 0 73 15.1 1 0 0 21 17 17 24 23 23 5 2 91 55 0 0 91 17.75 1 0 0 23 19 16 24 24 21 4 26 108 62 0 0 108 15.2 1 0 1 22 20 18 21 21 23 4 20 105 51 105 0 105 14.6 1 1 0 16 16 12 20 15 20 5 23 117 30 0 117 117 16.65 1 0 0 20 20 17 26 22 23 8 22 119 49 0 0 119 8.1 1 0 1 16 16 13 16 25 24 15 24 31 16 31 0 31
Names of X columns:
TOT YearN GroupN gender AMS.I1 AMS.I2 AMS.I3 AMS.E1 AMS.E2 AMS.E3 AMS.A NUMERACYTOT LFM CH Gender_LFM Group_LFM Year_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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Raw Output
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Big Analytics Cloud Computing Center
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