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7.5 1.5 1.8 2011 1 0 68 149 18 4 21 13 12
6 2.1 2.1 2011 1 1 39 139 31 4 22 8 8
6.5 2.1 2.2 2011 1 0 32 148 39 5 22 14 11
1 1.9 2.3 2011 1 1 62 158 46 4 18 16 13
1 1.6 2.1 2011 1 1 33 128 31 4 23 14 11
5.5 2.1 2.7 2011 1 1 52 224 67 9 12 13 10
8.5 2.1 2.1 2011 1 0 62 159 35 8 20 15 7
6.5 2.2 2.4 2011 1 1 77 105 52 11 22 13 10
4.5 1.5 2.9 2011 1 1 76 159 77 4 21 20 15
2 1.9 2.2 2011 1 1 41 167 37 4 19 17 12
5 2.2 2.1 2011 1 1 48 165 32 6 22 15 12
0.5 1.6 2.2 2011 1 1 63 159 36 4 15 16 10
5 1.5 2.2 2011 1 1 30 119 38 8 20 12 10
5 1.9 2.7 2011 1 0 78 176 69 4 19 17 14
2.5 0.1 1.9 2011 1 0 19 54 21 4 18 11 6
5 2.2 2 2011 0 0 31 91 26 11 15 16 12
5.5 1.8 2.5 2011 1 1 66 163 54 4 20 16 14
3.5 1.6 2.2 2011 1 0 35 124 36 4 21 15 11
3 2.2 2.3 2011 0 1 42 137 42 6 21 13 8
4 2.1 1.9 2011 1 0 45 121 23 6 15 14 12
0.5 1.9 2.1 2011 1 1 21 153 34 4 16 19 15
6.5 1.6 3.5 2011 1 1 25 148 112 8 23 16 13
4.5 1.9 2.1 2011 1 0 44 221 35 5 21 17 11
7.5 2.2 2.3 2011 1 1 69 188 47 4 18 10 12
5.5 1.8 2.3 2011 1 1 54 149 47 9 25 15 7
4 2.4 2.2 2011 1 1 74 244 37 4 9 14 11
7.5 2.4 3.5 2011 0 1 80 148 109 7 30 14 7
7 2.5 1.9 2011 0 0 42 92 24 10 20 16 12
4 1.9 1.9 2011 1 1 61 150 20 4 23 15 12
5.5 2.1 1.9 2011 1 0 41 153 22 4 16 17 13
2.5 1.9 1.9 2011 1 0 46 94 23 7 16 14 9
5.5 2.1 2.1 2011 1 0 39 156 32 12 19 16 11
3.5 1.5 2 2011 1 1 34 132 30 7 25 15 12
2.5 1.9 3.2 2011 1 1 51 161 92 5 18 16 15
4.5 2.1 2.3 2011 1 1 42 105 43 8 23 16 12
4.5 1.5 2.5 2011 1 1 31 97 55 5 21 10 6
4.5 2.1 1.8 2011 1 0 39 151 16 4 10 8 5
6 2.1 2.4 2011 0 1 20 131 49 9 14 17 13
2.5 1.8 2.8 2011 1 1 49 166 71 7 22 14 11
5 2.4 2.3 2011 1 0 53 157 43 4 26 10 6
0 2.1 2 2011 1 1 31 111 29 4 23 14 12
5 1.9 2.5 2011 1 1 39 145 56 4 23 12 10
6.5 2.1 2.3 2011 1 1 54 162 46 4 24 16 6
5 1.9 1.8 2011 1 1 49 163 19 4 24 16 12
6 2.4 1.9 2011 0 1 34 59 23 7 18 16 11
4.5 2.1 2.6 2011 1 0 46 187 59 4 23 8 6
5.5 2.2 2 2011 1 1 55 109 30 7 15 16 12
1 2.2 2.6 2011 0 1 42 90 61 4 19 15 12
7.5 1.8 1.6 2011 1 0 50 105 7 4 16 8 8
6 2.1 2.2 2011 0 1 13 83 38 4 25 13 10
5 2.4 2.1 2011 0 1 37 116 32 4 23 14 11
1 2.2 1.8 2011 0 1 25 42 16 8 17 13 7
5 2.1 1.8 2011 1 1 30 148 19 4 19 16 12
6.5 1.5 1.9 2011 0 1 28 155 22 4 21 19 13
7 1.9 2.4 2011 1 1 45 125 48 4 18 19 14
4.5 1.8 1.9 2011 1 1 35 116 23 4 27 14 12
0 1.8 2 2011 0 0 28 128 26 7 21 15 6
8.5 1.6 2.1 2011 1 1 41 138 33 12 13 13 14
3.5 1.2 1.7 2011 0 0 6 49 9 4 8 10 10
7.5 1.8 1.9 2011 0 1 45 96 24 4 29 16 12
3.5 1.5 2.1 2011 1 1 73 164 34 4 28 15 11
6 2.1 2.4 2011 1 0 17 162 48 5 23 11 10
1.5 2.4 1.8 2011 1 0 40 99 18 15 21 9 7
9 2.4 2.3 2011 1 1 64 202 43 5 19 16 12
3.5 1.5 2.1 2011 1 0 37 186 33 10 19 12 7
3.5 1.8 2 2011 0 1 25 66 28 9 20 12 12
4 2.1 2.8 2011 1 0 65 183 71 8 18 14 12
6.5 2.2 2 2011 1 1 100 214 26 4 19 14 10
7.5 2.1 2.7 2011 1 1 28 188 67 5 17 13 10
6 1.9 2.1 2011 0 0 35 104 34 4 19 15 12
5 2.1 2.9 2011 1 0 56 177 80 9 25 17 12
5.5 1.9 2 2011 1 0 29 126 29 4 19 14 12
3.5 1.6 1.8 2011 0 0 43 76 16 10 22 11 8
7.5 2.4 2.6 2011 0 1 59 99 59 4 23 9 10
6.5 1.9 2.1 2011 1 0 50 139 32 4 14 7 5
6.5 2.1 2.3 2011 1 0 59 162 43 7 16 15 10
6.5 1.8 2.2 2011 0 1 27 108 38 5 24 12 12
7 2.1 2 2011 1 0 61 159 29 4 20 15 11
3.5 2.4 2.2 2011 0 0 28 74 36 4 12 14 9
1.5 2.1 2.1 2011 1 1 51 110 32 4 24 16 12
4 2.2 2.1 2011 0 0 35 96 35 4 22 14 11
7.5 2.1 1.9 2011 0 0 29 116 21 4 12 13 10
4.5 2.2 2 2011 0 0 48 87 29 4 22 16 12
0 1.6 1.7 2011 0 1 25 97 12 6 20 13 10
3.5 2.4 2.2 2011 0 0 44 127 37 10 10 16 9
5.5 2.1 2.2 2011 0 1 64 106 37 7 23 16 11
5 1.9 2.3 2011 0 1 32 80 47 4 17 16 12
4.5 2.4 2.4 2011 0 0 20 74 51 4 22 10 7
2.5 2.1 2.1 2011 0 0 28 91 32 7 24 12 11
7.5 1.8 1.9 2011 0 0 34 133 21 4 18 12 12
7 2.1 1.7 2011 0 1 31 74 13 8 21 12 6
0 1.8 1.8 2011 0 1 26 114 14 11 20 12 9
4.5 1.9 1.5 2011 0 1 58 140 -2 6 20 19 15
3 1.9 1.9 2011 0 0 23 95 20 14 22 14 10
1.5 2.4 1.9 2011 0 1 21 98 24 5 19 13 11
3.5 1.8 1.7 2011 0 0 21 121 11 4 20 16 12
2.5 1.8 1.9 2011 0 1 33 126 23 8 26 15 12
5.5 2.1 1.9 2011 0 1 16 98 24 9 23 12 12
8 2.1 1.8 2011 0 1 20 95 14 4 24 8 11
1 2.4 2.4 2011 0 1 37 110 52 4 21 10 9
5 1.9 1.8 2011 0 1 35 70 15 5 21 16 11
4.5 1.8 1.9 2011 0 0 33 102 23 4 19 16 12
3 1.8 1.8 2011 0 1 27 86 19 5 8 10 12
3 2.2 2.1 2011 0 1 41 130 35 4 17 18 14
8 2.4 1.9 2011 0 1 40 96 24 4 20 12 8
2.5 1.8 2.2 2011 0 0 35 102 39 7 11 16 10
7 2.4 2 2011 0 0 28 100 29 10 8 10 9
0 1.8 1.7 2011 0 0 32 94 13 4 15 14 10
1 1.9 1.7 2011 0 0 22 52 8 5 18 12 9
3.5 2.4 1.8 2011 0 0 44 98 18 4 18 11 10
5.5 2.1 1.9 2011 0 0 27 118 24 4 19 15 12
5.5 1.9 1.8 2011 0 1 17 99 19 4 19 7 11
0.5 2.1 1 2012 1 1 12 48 23 6 23 16 9
7.5 2.7 1 2012 1 1 45 50 16 4 22 16 11
9 2.1 4 2012 1 1 37 150 33 8 21 16 12
9.5 2.1 4 2012 1 1 37 154 32 5 25 16 12
8.5 2.1 3 2012 0 0 108 109 37 4 30 12 7
7 2.1 2 2012 0 1 10 68 14 17 17 15 12
8 2.1 4 2012 1 1 68 194 52 4 27 14 12
10 2.1 4 2012 1 0 72 158 75 4 23 15 12
7 2.1 4 2012 1 1 143 159 72 8 23 16 10
8.5 2.1 2 2012 1 0 9 67 15 4 18 13 15
9 2.4 4 2012 1 0 55 147 29 7 18 10 10
9.5 1.95 1 2012 1 1 17 39 13 4 23 17 15
4 2.1 3 2012 1 1 37 100 40 4 19 15 10
6 2.1 3 2012 1 1 27 111 19 5 15 18 15
8 1.95 4 2012 1 1 37 138 24 7 20 16 9
5.5 2.1 3 2012 1 1 58 101 121 4 16 20 15
9.5 2.4 4 2012 0 1 66 131 93 4 24 16 12
7.5 2.1 3 2012 1 1 21 101 36 7 25 17 13
7 2.25 3 2012 1 1 19 114 23 11 25 16 12
7.5 2.4 4 2012 1 0 78 165 85 7 19 15 12
8 2.25 3 2012 1 1 35 114 41 4 19 13 8
7 2.55 3 2012 1 1 48 111 46 4 16 16 9
7 1.95 2 2012 1 1 27 75 18 4 19 16 15
6 2.4 2 2012 1 1 43 82 35 4 19 16 12
10 2.1 3 2012 1 1 30 121 17 4 23 17 12
2.5 2.1 1 2012 1 1 25 32 4 4 21 20 15
9 2.4 4 2012 1 0 69 150 28 6 22 14 11
8 2.1 3 2012 1 1 72 117 44 8 19 17 12
6 2.1 2 2012 0 1 23 71 10 23 20 6 6
8.5 2.25 4 2012 1 1 13 165 38 4 20 16 14
6 2.25 4 2012 1 1 61 154 57 8 3 15 12
9 2.4 4 2012 1 1 43 126 23 6 23 16 12
8 2.1 4 2012 1 0 51 149 36 4 23 16 12
9 2.4 4 2012 1 0 67 145 22 7 20 14 11
5.5 2.1 3 2012 1 1 36 120 40 4 15 16 12
7 2.1 3 2012 1 0 44 109 31 4 16 16 12
5.5 2.25 4 2012 1 0 45 132 11 4 7 16 12
9 2.25 4 2012 1 1 34 172 38 10 24 14 12
2 2.4 4 2012 1 0 36 169 24 6 17 14 8
8.5 2.25 3 2012 1 1 72 114 37 5 24 16 8
9 2.25 4 2012 1 1 39 156 37 5 24 16 12
8.5 2.1 4 2012 1 0 43 172 22 4 19 15 12
9 2.1 2 2012 0 1 25 68 15 4 25 16 11
7.5 2.1 2 2012 0 1 56 89 2 5 20 16 10
10 2.7 4 2012 1 1 80 167 43 5 28 18 11
9 2.1 3 2012 1 0 40 113 31 5 23 15 12
7.5 2.1 3 2012 0 0 73 115 29 5 27 16 13
6 2.25 2 2012 0 0 34 78 45 4 18 16 12
10.5 2.7 3 2012 0 0 72 118 25 6 28 16 12
8.5 2.4 2 2012 0 1 42 87 4 4 21 17 10
8 2.1 4 2012 1 0 61 173 31 4 19 14 10
10 2.1 1 2012 1 1 23 2 -4 4 23 18 11
10.5 2.4 4 2012 0 0 74 162 66 9 27 9 8
6.5 1.95 1 2012 0 1 16 49 61 18 22 15 12
9.5 2.7 4 2012 0 0 66 122 32 6 28 14 9
8.5 2.1 3 2012 0 1 9 96 31 5 25 15 12
7.5 2.25 3 2012 0 0 41 100 39 4 21 13 9
5 2.1 2 2012 0 0 57 82 19 11 22 16 11
8 2.7 3 2012 0 1 48 100 31 4 28 20 15
10 2.1 3 2012 0 0 51 115 36 10 20 14 8
7 2.1 4 2012 0 1 53 141 42 6 29 12 8
7.5 1.65 4 2012 1 1 29 165 21 8 25 15 11
7.5 1.65 4 2012 1 1 29 165 21 8 25 15 11
9.5 2.1 3 2012 0 1 55 110 25 6 20 15 11
6 2.1 3 2012 1 1 54 118 32 8 20 16 13
10 2.1 4 2012 1 0 43 158 26 4 16 11 7
7 2.1 4 2012 0 1 51 146 28 4 20 16 12
3 2.1 1 2012 1 0 20 49 32 9 20 7 8
6 2.4 2 2012 0 0 79 90 41 9 23 11 8
7 2.4 3 2012 0 0 39 121 29 5 18 9 4
10 2.1 4 2012 1 1 61 155 33 4 25 15 11
7 2.25 3 2012 0 0 55 104 17 4 18 16 10
3.5 2.4 4 2012 0 1 30 147 13 15 19 14 7
8 2.1 3 2012 0 0 55 110 32 10 25 15 12
10 2.1 3 2012 0 0 22 108 30 9 25 13 11
5.5 2.4 3 2012 0 0 37 113 34 7 25 13 9
6 2.4 3 2012 0 0 2 115 59 9 24 12 10
6.5 2.1 1 2012 0 1 38 61 13 6 19 16 8
6.5 2.1 1 2012 0 1 27 60 23 4 26 14 8
8.5 2.4 3 2012 0 1 56 109 10 7 10 16 11
4 2.1 2 2012 0 1 25 68 5 4 17 14 12
9.5 2.7 3 2012 0 0 39 111 31 7 13 15 10
8 2.1 2 2012 0 0 33 77 19 4 17 10 10
8.5 2.1 2 2012 0 1 43 73 32 15 30 16 12
5.5 2.25 4 2012 1 0 57 151 30 4 25 14 8
7 2.1 2 2012 0 0 43 89 25 9 4 16 11
9 2.4 2 2012 0 0 23 78 48 4 16 12 8
8 2.25 3 2012 0 0 44 110 35 4 21 16 10
10 2.25 4 2012 1 1 54 220 67 28 23 16 14
8 2.1 2 2012 0 1 28 65 15 4 22 15 9
6 2.1 4 2012 1 0 36 141 22 4 17 14 9
8 2.4 3 2012 0 0 39 117 18 4 20 16 10
5 2.25 4 2012 1 1 16 122 33 5 20 11 13
9 2.1 2 2012 0 0 23 63 46 4 22 15 12
4.5 2.1 1 2012 1 1 40 44 24 4 16 18 13
8.5 1.65 1 2012 0 1 24 52 14 12 23 13 8
9.5 2.7 4 2012 0 0 78 131 12 4 0 7 3
8.5 2.1 3 2012 0 1 57 101 38 6 18 7 8
7.5 1.95 1 2012 0 1 37 42 12 6 25 17 12
7.5 2.25 4 2012 1 1 27 152 28 5 23 18 11
5 2.4 3 2012 1 0 61 107 41 4 12 15 9
7 1.95 2 2012 0 0 27 77 12 4 18 8 12
8 2.1 4 2012 1 0 69 154 31 4 24 13 12
5.5 2.4 3 2012 1 1 34 103 33 10 11 13 12
8.5 2.1 3 2012 0 1 44 96 34 7 18 15 10
9.5 2.4 4 2012 1 1 34 175 21 4 23 18 13
7 2.4 1 2012 0 1 39 57 20 7 24 16 9
8 2.4 3 2012 0 0 51 112 44 4 29 14 12
8.5 2.25 4 2012 1 0 34 143 52 4 18 15 11
3.5 2.4 1 2012 0 0 31 49 7 12 15 19 14
6.5 2.1 3 2012 1 1 13 110 29 5 29 16 11
6.5 2.1 4 2012 1 1 12 131 11 8 16 12 9
10.5 1.8 4 2012 1 0 51 167 26 6 19 16 12
8.5 2.7 1 2012 0 0 24 56 24 17 22 11 8
8 2.1 4 2012 1 0 19 137 7 4 16 16 15
10 2.1 2 2012 0 1 30 86 60 5 23 15 12
10 2.4 3 2012 1 1 81 121 13 4 23 19 14
9.5 2.55 4 2012 1 0 42 149 20 5 19 15 12
9 2.55 4 2012 1 0 22 168 52 5 4 14 9
10 2.1 4 2012 1 0 85 140 28 6 20 14 9
7.5 2.1 2 2012 0 1 27 88 25 4 24 17 13
4.5 2.1 4 2012 1 1 25 168 39 4 20 16 13
4.5 2.25 2 2012 1 1 22 94 9 4 4 20 15
0.5 2.25 1 2012 1 1 19 51 19 6 24 16 11
6.5 2.1 1 2012 0 0 14 48 13 8 22 9 7
4.5 2.1 4 2012 1 1 45 145 60 10 16 13 10
5.5 1.95 2 2012 1 1 45 66 19 4 3 15 11
5 2.4 2 2012 0 1 28 85 34 5 15 19 14
6 2.1 3 2012 1 0 51 109 14 4 24 16 14
4 2.4 2 2012 0 0 41 63 17 4 17 17 13
8 2.4 3 2012 0 1 31 102 45 4 20 16 12
10.5 2.4 4 2012 0 0 74 162 66 16 27 9 8
6.5 1.95 2 2012 0 1 19 86 48 7 26 11 13
8 2.1 3 2012 0 1 51 114 29 4 23 14 9
8.5 2.1 4 2012 1 0 73 164 -2 4 17 19 12
5.5 2.55 3 2012 1 1 24 119 51 14 20 13 13
7 2.1 4 2012 1 0 61 126 2 5 22 14 11
5 2.1 4 2012 1 1 23 132 24 5 19 15 11
3.5 2.1 4 2012 1 1 14 142 40 5 24 15 13
5 1.95 2 2012 1 0 54 83 20 5 19 14 12
9 2.25 2 2012 0 1 51 94 19 7 23 16 12
8.5 2.4 2 2012 0 0 62 81 16 19 15 17 10
5 1.95 4 2012 1 1 36 166 20 16 27 12 9
9.5 2.1 3 2012 0 0 59 110 40 4 26 15 10
3 2.1 2 2012 0 1 24 64 27 4 22 17 13
1.5 1.95 2 2012 1 0 26 93 25 7 22 15 13
6 2.1 3 2012 0 0 54 104 49 9 18 10 9
0.5 2.1 3 2012 0 1 39 105 39 5 15 16 11
6.5 1.95 1 2012 0 1 16 49 61 14 22 15 12
7.5 2.1 2 2012 0 0 36 88 19 4 27 11 8
4.5 1.95 2 2012 0 1 31 95 67 16 10 16 12
8 2.4 3 2012 0 1 31 102 45 10 20 16 12
9 2.4 3 2012 0 0 42 99 30 5 17 16 12
7.5 2.4 2 2012 0 1 39 63 8 6 23 14 9
8.5 1.95 2 2012 0 0 25 76 19 4 19 14 12
7 2.7 3 2012 0 0 31 109 52 4 13 16 12
9.5 2.1 3 2012 0 1 38 117 22 4 27 16 11
6.5 1.95 1 2012 0 1 31 57 17 5 23 18 12
9.5 2.1 3 2012 0 0 17 120 33 4 16 14 6
6 1.95 2 2012 0 1 22 73 34 4 25 20 7
8 2.1 2 2012 0 0 55 91 22 5 2 15 10
9.5 2.25 3 2012 0 0 62 108 30 4 26 16 12
8 2.7 3 2012 0 1 51 105 25 4 20 16 10
8 2.1 3 2012 1 0 30 117 38 5 23 16 12
9 2.4 3 2012 0 0 49 119 26 8 22 12 9
5 1.35 1 2012 0 1 16 31 13 15 24 8 3

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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$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$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}