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Data X:
'12.9' 2011 1 0 11 8 7 18 12 20 4 21 149 68 '12.2' 2011 1 1 19 18 20 23 20 19 4 22 139 39 '12.8' 2011 1 0 16 12 9 22 14 18 5 22 148 32 '7.4' 2011 1 1 24 24 19 22 25 24 4 18 158 62 '6.7' 2011 1 1 15 16 12 19 15 20 4 23 128 33 '12.6' 2011 1 1 17 19 16 25 20 20 9 12 224 52 '14.8' 2011 1 0 19 16 17 28 21 24 8 20 159 62 '13.3' 2011 1 1 19 15 9 16 15 21 11 22 105 77 '11.1' 2011 1 1 28 28 28 28 28 28 4 21 159 76 '8.2' 2011 1 1 26 21 20 21 11 10 4 19 167 41 '11.4' 2011 1 1 15 18 16 22 22 22 6 22 165 48 '6.4' 2011 1 1 26 22 22 24 22 19 4 15 159 63 '10.6' 2011 1 1 16 19 17 24 27 27 8 20 119 30 '12.0' 2011 1 0 24 22 12 26 24 23 4 19 176 78 '6.3' 2011 1 0 25 25 18 28 23 24 4 18 54 19 '11.3' 2011 0 0 22 20 20 24 24 24 11 15 91 31 '11.9' 2011 1 1 15 16 12 20 21 25 4 20 163 66 '9.3' 2011 1 0 21 19 16 26 20 24 4 21 124 35 '9.6' 2011 0 1 22 18 16 21 19 21 6 21 137 42 '10.0' 2011 1 0 27 26 21 28 25 28 6 15 121 45 '6.4' 2011 1 1 26 24 15 27 16 28 4 16 153 21 '13.8' 2011 1 1 26 20 17 23 24 22 8 23 148 25 '10.8' 2011 1 0 22 19 17 24 21 26 5 21 221 44 '13.8' 2011 1 1 21 19 17 24 22 26 4 18 188 69 '11.7' 2011 1 1 22 23 18 22 25 21 9 25 149 54 '10.9' 2011 1 1 20 18 15 21 23 26 4 9 244 74 '16.1' 2011 0 1 21 16 20 25 20 23 7 30 148 80 '13.4' 2011 0 0 20 18 13 20 21 20 10 20 92 42 '9.9' 2011 1 1 22 21 21 21 22 24 4 23 150 61 '11.5' 2011 1 0 21 20 12 26 25 25 4 16 153 41 '8.3' 2011 1 0 8 15 6 23 23 24 7 16 94 46 '11.7' 2011 1 0 22 19 13 21 19 20 12 19 156 39 '9.0' 2011 1 1 20 19 19 27 21 24 7 25 132 34 '9.7' 2011 1 1 24 7 12 25 19 25 5 18 161 51 '10.8' 2011 1 1 17 20 14 23 25 23 8 23 105 42 '10.3' 2011 1 1 20 20 13 25 16 21 5 21 97 31 '10.4' 2011 1 0 23 19 12 23 24 23 4 10 151 39 '12.7' 2011 0 1 20 19 17 19 24 21 9 14 131 20 '9.3' 2011 1 1 22 20 19 22 18 18 7 22 166 49 '11.8' 2011 1 0 19 18 10 24 28 24 4 26 157 53 '5.9' 2011 1 1 15 14 10 19 15 18 4 23 111 31 '11.4' 2011 1 1 20 17 11 21 17 21 4 23 145 39 '13.0' 2011 1 1 22 17 11 27 18 23 4 24 162 54 '10.8' 2011 1 1 17 8 10 25 26 25 4 24 163 49 '12.3' 2011 0 1 14 9 7 25 18 22 7 18 59 34 '11.3' 2011 1 0 24 22 22 23 22 22 4 23 187 46 '11.8' 2011 1 1 17 20 12 17 19 23 7 15 109 55 '7.9' 2011 0 1 23 20 18 28 17 24 4 19 90 42 '12.7' 2011 1 0 25 22 20 25 26 25 4 16 105 50 '12.3' 2011 0 1 16 22 9 20 21 22 4 25 83 13 '11.6' 2011 0 1 18 22 16 25 26 24 4 23 116 37 '6.7' 2011 0 1 20 16 14 21 21 21 8 17 42 25 '10.9' 2011 1 1 18 14 11 24 12 24 4 19 148 30 '12.1' 2011 0 1 23 24 20 28 20 25 4 21 155 28 '13.3' 2011 1 1 24 21 17 20 20 23 4 18 125 45 '10.1' 2011 1 1 23 20 14 19 24 27 4 27 116 35 '5.7' 2011 0 0 13 20 8 24 24 27 7 21 128 28 '14.3' 2011 1 1 20 18 16 21 22 23 12 13 138 41 '8.0' 2011 0 0 20 14 11 24 21 18 4 8 49 6 '13.3' 2011 0 1 19 19 10 23 20 20 4 29 96 45 '9.3' 2011 1 1 22 24 15 18 23 23 4 28 164 73 '12.5' 2011 1 0 22 19 15 27 19 24 5 23 162 17 '7.6' 2011 1 0 15 16 10 25 24 26 15 21 99 40 '15.9' 2011 1 1 17 16 10 20 21 20 5 19 202 64 '9.2' 2011 1 0 19 16 18 21 16 23 10 19 186 37 '9.1' 2011 0 1 20 14 10 23 17 22 9 20 66 25 '11.1' 2011 1 0 22 22 22 27 23 23 8 18 183 65 '13.0' 2011 1 1 21 21 16 24 20 17 4 19 214 100 '14.5' 2011 1 1 21 15 10 27 19 20 5 17 188 28 '12.2' 2011 0 0 16 14 7 24 18 22 4 19 104 35 '12.3' 2011 1 0 20 15 16 23 18 18 9 25 177 56 '11.4' 2011 1 0 21 14 16 24 21 19 4 19 126 29 '8.8' 2011 0 0 20 20 16 21 20 19 10 22 76 43 '14.6' 2011 0 1 23 21 22 23 17 16 4 23 99 59 '12.6' 2011 1 0 18 14 5 27 25 26 4 14 139 50 '13.0' 2011 1 0 16 16 10 25 17 25 7 16 162 59 '12.6' 2011 0 1 17 13 8 19 17 23 5 24 108 27 '13.2' 2011 1 0 24 26 16 24 24 18 4 20 159 61 '9.9' 2011 0 0 13 13 8 25 21 22 4 12 74 28 '7.7' 2011 1 1 19 18 16 23 22 26 4 24 110 51 '10.5' 2011 0 0 20 15 14 23 18 25 4 22 96 35 '13.4' 2011 0 0 22 18 15 25 22 26 4 12 116 29 '10.9' 2011 0 0 19 21 9 26 20 26 4 22 87 48 '4.3' 2011 0 1 21 17 21 26 21 24 6 20 97 25 '10.3' 2011 0 0 15 18 7 16 21 22 10 10 127 44 '11.8' 2011 0 1 21 20 17 23 20 21 7 23 106 64 '11.2' 2011 0 1 24 18 18 26 18 22 4 17 80 32 '11.4' 2011 0 0 22 25 16 25 25 28 4 22 74 20 '8.6' 2011 0 0 20 20 16 23 23 22 7 24 91 28 '13.2' 2011 0 0 21 19 14 26 21 26 4 18 133 34 '12.6' 2011 0 1 19 18 15 22 20 20 8 21 74 31 '5.6' 2011 0 1 14 12 8 20 21 24 11 20 114 26 '9.9' 2011 0 1 25 22 22 27 20 21 6 20 140 58 '8.8' 2011 0 0 11 16 5 20 22 23 14 22 95 23 '7.7' 2011 0 1 17 18 13 22 15 23 5 19 98 21 '9.0' 2011 0 0 22 23 22 24 24 23 4 20 121 21 '7.3' 2011 0 1 20 20 18 21 22 22 8 26 126 33 '11.4' 2011 0 1 22 20 15 24 21 23 9 23 98 16 '13.6' 2011 0 1 15 16 11 26 17 21 4 24 95 20 '7.9' 2011 0 1 23 22 19 24 23 27 4 21 110 37 '10.7' 2011 0 1 20 19 19 24 22 23 5 21 70 35 '10.3' 2011 0 0 22 23 21 27 23 26 4 19 102 33 '8.3' 2011 0 1 16 6 4 25 16 27 5 8 86 27 '9.6' 2011 0 1 25 19 17 27 18 27 4 17 130 41 '14.2' 2011 0 1 18 24 10 19 25 23 4 20 96 40 '8.5' 2011 0 0 19 19 13 22 18 23 7 11 102 35 '13.5' 2011 0 0 25 15 15 22 14 23 10 8 100 28 '4.9' 2011 0 0 21 18 11 25 20 28 4 15 94 32 '6.4' 2011 0 0 22 18 20 23 19 24 5 18 52 22 '9.6' 2011 0 0 21 22 13 24 18 20 4 18 98 44 '11.6' 2011 0 0 22 23 18 24 22 23 4 19 118 27 '11.1' 2011 0 1 23 18 20 23 21 22 4 19 99 17 '4.35' 2012 1 1 20 17 15 22 14 15 6 23 48 12 '12.7' 2012 1 1 6 6 4 24 5 27 4 22 50 45 '18.1' 2012 1 1 15 22 9 19 25 23 8 21 150 37 '17.85' 2012 1 1 18 20 18 25 21 23 5 25 154 37 '16.6' 2012 0 0 24 16 12 26 11 20 4 30 109 108 '12.6' 2012 0 1 22 16 17 18 20 18 17 17 68 10 '17.1' 2012 1 1 21 17 12 24 9 22 4 27 194 68 '19.1' 2012 1 0 23 20 16 28 15 20 4 23 158 72 '16.1' 2012 1 1 20 23 17 23 23 21 8 23 159 143 '13.35' 2012 1 0 20 18 14 19 21 25 4 18 67 9 '18.4' 2012 1 0 18 13 13 19 9 19 7 18 147 55 '14.7' 2012 1 1 25 22 20 27 24 25 4 23 39 17 '10.6' 2012 1 1 16 20 16 24 16 24 4 19 100 37 '12.6' 2012 1 1 20 20 15 26 20 22 5 15 111 27 '16.2' 2012 1 1 14 13 10 21 15 28 7 20 138 37 '13.6' 2012 1 1 22 16 16 25 18 22 4 16 101 58 '18.9' 2012 0 1 26 25 21 28 22 21 4 24 131 66 '14.1' 2012 1 1 20 16 15 19 21 23 7 25 101 21 '14.5' 2012 1 1 17 15 16 20 21 19 11 25 114 19 '16.15' 2012 1 0 22 19 19 26 21 21 7 19 165 78 '14.75' 2012 1 1 22 19 9 27 20 25 4 19 114 35 '14.8' 2012 1 1 20 24 19 23 24 23 4 16 111 48 '12.45' 2012 1 1 17 9 7 18 15 28 4 19 75 27 '12.65' 2012 1 1 22 22 23 23 24 14 4 19 82 43 '17.35' 2012 1 1 17 15 14 21 18 23 4 23 121 30 '8.6' 2012 1 1 22 22 10 23 24 24 4 21 32 25 '18.4' 2012 1 0 21 22 16 22 24 25 6 22 150 69 '16.1' 2012 1 1 25 24 12 21 15 15 8 19 117 72 '11.6' 2012 0 1 11 12 10 14 19 23 23 20 71 23 '17.75' 2012 1 1 19 21 7 24 20 26 4 20 165 13 '15.25' 2012 1 1 24 25 20 26 26 21 8 3 154 61 '17.65' 2012 1 1 17 26 9 24 26 26 6 23 126 43 '16.35' 2012 1 0 22 21 12 22 23 23 4 23 149 51 '17.65' 2012 1 0 17 14 10 20 13 15 7 20 145 67 '13.6' 2012 1 1 26 28 19 20 16 16 4 15 120 36 '14.35' 2012 1 0 20 21 11 18 22 20 4 16 109 44 '14.75' 2012 1 0 19 16 15 18 21 20 4 7 132 45 '18.25' 2012 1 1 21 16 14 25 11 21 10 24 172 34 '9.9' 2012 1 0 24 25 11 28 23 28 6 17 169 36 16 2012 1 1 21 21 14 23 18 19 5 24 114 72 '18.25' 2012 1 1 19 22 15 20 19 21 5 24 156 39 '16.85' 2012 1 0 13 9 7 22 15 22 4 19 172 43 '14.6' 2012 0 1 24 20 22 27 8 27 4 25 68 25 '13.85' 2012 0 1 28 19 19 24 15 20 5 20 89 56 '18.95' 2012 1 1 27 24 22 23 21 17 5 28 167 80 '15.6' 2012 1 0 22 22 11 20 25 26 5 23 113 40 '14.85' 2012 0 0 23 22 19 22 14 21 5 27 115 73 '11.75' 2012 0 0 19 12 9 21 21 24 4 18 78 34 '18.45' 2012 0 0 18 17 11 24 18 21 6 28 118 72 '15.9' 2012 0 1 23 18 17 26 18 25 4 21 87 42 '17.1' 2012 1 0 21 10 12 24 12 22 4 19 173 61 '16.1' 2012 1 1 22 22 17 18 24 17 4 23 2 23 '19.9' 2012 0 0 17 24 10 17 17 14 9 27 162 74 '10.95' 2012 0 1 15 18 17 23 20 23 18 22 49 16 '18.45' 2012 0 0 21 18 13 21 24 28 6 28 122 66 '15.1' 2012 0 1 20 23 11 21 22 24 5 25 96 9 15 2012 0 0 26 21 19 24 15 22 4 21 100 41 '11.35' 2012 0 0 19 21 21 22 22 24 11 22 82 57 '15.95' 2012 0 1 28 28 24 24 26 25 4 28 100 48 '18.1' 2012 0 0 21 17 13 24 17 21 10 20 115 51 '14.6' 2012 0 1 19 21 16 24 23 22 6 29 141 53 '15.4' 2012 1 1 22 21 13 23 19 16 8 25 165 29 '15.4' 2012 1 1 21 20 15 21 21 18 8 25 165 29 '17.6' 2012 0 1 20 18 15 24 23 27 6 20 110 55 '13.35' 2012 1 1 19 17 11 19 19 17 8 20 118 54 '19.1' 2012 1 0 11 7 7 19 18 25 4 16 158 43 '15.35' 2012 0 1 17 17 13 23 16 24 4 20 146 51 '7.6' 2012 1 0 19 14 13 25 23 21 9 20 49 20 '13.4' 2012 0 0 20 18 12 24 13 21 9 23 90 79 '13.9' 2012 0 0 17 14 8 21 18 19 5 18 121 39 '19.1' 2012 1 1 21 23 7 18 23 27 4 25 155 61 '15.25' 2012 0 0 21 20 17 23 21 28 4 18 104 55 '12.9' 2012 0 1 12 14 9 20 23 19 15 19 147 30 '16.1' 2012 0 0 23 17 18 23 16 23 10 25 110 55 '17.35' 2012 0 0 22 21 17 23 17 25 9 25 108 22 '13.15' 2012 0 0 22 23 17 23 20 26 7 25 113 37 '12.15' 2012 0 0 21 24 18 23 18 25 9 24 115 2 '12.6' 2012 0 1 20 21 12 27 20 25 6 19 61 38 '10.35' 2012 0 1 18 14 14 19 19 24 4 26 60 27 '15.4' 2012 0 1 21 24 22 25 26 24 7 10 109 56 '9.6' 2012 0 1 24 16 19 25 9 24 4 17 68 25 '18.2' 2012 0 0 22 21 21 21 23 22 7 13 111 39 '13.6' 2012 0 0 20 8 10 25 9 21 4 17 77 33 '14.85' 2012 0 1 17 17 16 17 13 17 15 30 73 43 '14.75' 2012 1 0 19 18 11 22 27 23 4 25 151 57 '14.1' 2012 0 0 16 17 15 23 22 17 9 4 89 43 '14.9' 2012 0 0 19 16 12 27 12 25 4 16 78 23 '16.25' 2012 0 0 23 22 21 27 18 19 4 21 110 44 '19.25' 2012 1 1 8 17 22 5 6 8 28 23 220 54 '13.6' 2012 0 1 22 21 20 19 17 14 4 22 65 28 '13.6' 2012 1 0 23 20 15 24 22 22 4 17 141 36 '15.65' 2012 0 0 15 20 9 23 22 25 4 20 117 39 '12.75' 2012 1 1 17 19 15 28 23 28 5 20 122 16 '14.6' 2012 0 0 21 8 14 25 19 25 4 22 63 23 '9.85' 2012 1 1 25 19 11 27 20 24 4 16 44 40 '12.65' 2012 0 1 18 11 9 16 17 15 12 23 52 24 '19.2' 2012 0 0 20 13 12 25 24 24 4 0 131 78 '16.6' 2012 0 1 21 18 11 26 20 28 6 18 101 57 '11.2' 2012 0 1 21 19 14 24 18 24 6 25 42 37 '15.25' 2012 1 1 24 23 10 23 23 25 5 23 152 27 '11.9' 2012 1 0 22 20 18 24 27 23 4 12 107 61 '13.2' 2012 0 0 22 22 11 27 25 26 4 18 77 27 '16.35' 2012 1 0 23 19 14 25 24 26 4 24 154 69 '12.4' 2012 1 1 17 16 16 19 12 22 10 11 103 34 '15.85' 2012 0 1 15 11 11 19 16 25 7 18 96 44 '18.15' 2012 1 1 22 21 16 24 24 22 4 23 175 34 '11.15' 2012 0 1 19 14 13 20 23 26 7 24 57 39 '15.65' 2012 0 0 18 21 12 21 24 20 4 29 112 51 '17.75' 2012 1 0 21 20 17 28 24 26 4 18 143 34 '7.65' 2012 0 0 20 21 23 26 26 26 12 15 49 31 '12.35' 2012 1 1 19 20 14 19 19 21 5 29 110 13 '15.6' 2012 1 1 19 19 10 23 28 21 8 16 131 12 '19.3' 2012 1 0 16 19 16 23 23 24 6 19 167 51 '15.2' 2012 0 0 18 18 11 21 21 21 17 22 56 24 '17.1' 2012 1 0 23 20 16 26 19 18 4 16 137 19 '15.6' 2012 0 1 22 21 19 25 23 23 5 23 86 30 '18.4' 2012 1 1 23 22 17 25 23 26 4 23 121 81 '19.05' 2012 1 0 20 19 12 24 20 23 5 19 149 42 '18.55' 2012 1 0 24 23 17 23 18 25 5 4 168 22 '19.1' 2012 1 0 25 16 11 22 20 20 6 20 140 85 '13.1' 2012 0 1 25 23 19 27 28 25 4 24 88 27 '12.85' 2012 1 1 20 18 12 26 21 26 4 20 168 25 '9.5' 2012 1 1 23 23 8 23 25 19 4 4 94 22 '4.5' 2012 1 1 21 20 17 22 18 21 6 24 51 19 '11.85' 2012 0 0 23 20 13 26 24 23 8 22 48 14 '13.6' 2012 1 1 23 23 17 22 28 24 10 16 145 45 '11.7' 2012 1 1 11 13 7 17 9 6 4 3 66 45 '12.4' 2012 0 1 21 21 23 25 22 22 5 15 85 28 '13.35' 2012 1 0 27 26 18 22 26 21 4 24 109 51 '11.4' 2012 0 0 19 18 13 28 28 28 4 17 63 41 '14.9' 2012 0 1 21 19 17 22 18 24 4 20 102 31 '19.9' 2012 0 0 16 18 13 21 23 14 16 27 162 74 '11.2' 2012 0 1 21 18 8 24 15 20 7 26 86 19 '14.6' 2012 0 1 22 19 16 26 24 28 4 23 114 51 '17.6' 2012 1 0 16 13 14 26 12 19 4 17 164 73 '14.05' 2012 1 1 18 10 13 24 12 24 14 20 119 24 '16.1' 2012 1 0 23 21 19 27 20 21 5 22 126 61 '13.35' 2012 1 1 24 24 15 22 25 21 5 19 132 23 '11.85' 2012 1 1 20 21 15 23 24 26 5 24 142 14 '11.95' 2012 1 0 20 23 8 22 23 24 5 19 83 54 '14.75' 2012 0 1 18 18 14 23 18 26 7 23 94 51 '15.15' 2012 0 0 4 11 7 15 20 25 19 15 81 62 '13.2' 2012 1 1 14 16 11 20 22 23 16 27 166 36 '16.85' 2012 0 0 22 20 17 22 20 24 4 26 110 59 '7.85' 2012 0 1 17 20 19 25 25 24 4 22 64 24 '7.7' 2012 1 0 23 26 17 27 28 26 7 22 93 26 '12.6' 2012 0 0 20 21 12 24 25 23 9 18 104 54 '7.85' 2012 0 1 18 12 12 21 14 20 5 15 105 39 '10.95' 2012 0 1 19 15 18 17 16 16 14 22 49 16 '12.35' 2012 0 0 20 18 16 26 24 24 4 27 88 36 '9.95' 2012 0 1 15 14 15 20 13 20 16 10 95 31 '14.9' 2012 0 1 24 18 20 22 19 23 10 20 102 31 '16.65' 2012 0 0 21 16 16 24 18 23 5 17 99 42 '13.4' 2012 0 1 19 19 12 23 16 18 6 23 63 39 '13.95' 2012 0 0 19 7 10 22 8 21 4 19 76 25 '15.7' 2012 0 0 27 21 28 28 27 25 4 13 109 31 '16.85' 2012 0 1 23 24 19 21 23 23 4 27 117 38 '10.95' 2012 0 1 23 21 18 24 20 26 5 23 57 31 '15.35' 2012 0 0 20 20 19 28 20 26 4 16 120 17 '12.2' 2012 0 1 17 22 8 25 26 24 4 25 73 22 '15.1' 2012 0 0 21 17 17 24 23 23 5 2 91 55 '17.75' 2012 0 0 23 19 16 24 24 21 4 26 108 62 '15.2' 2012 0 1 22 20 18 21 21 23 4 20 105 51 '14.6' 2012 1 0 16 16 12 20 15 20 5 23 117 30 '16.65' 2012 0 0 20 20 17 26 22 23 8 22 119 49 '8.1' 2012 0 1 16 16 13 16 25 24 15 24 31 16
Names of X columns:
TOT Year GroupN gender AMS.I1 AMS.I2 AMS.I3 AMS.E1 AMS.E2 AMS.E3 AMS.A NUMERACYTOT LFM CH
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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