• QR Code (size=2)
• QR Code (size=3)
• QR Code (size=4)
• QR Code (size=5)
• QR Code (size=6)

7.5 149 18 1 0.5 0.67 0.67 0 0.5 1 0 11
6 139 31 0.89 0.5 0.83 0.33 0.5 1 1 1 19
6.5 148 39 0.89 0.4 1 0.67 0 1 1 0 16
1 158 46 0.89 0.5 0.83 0 0 0 1 1 24
1 128 31 0.89 0.7 0.67 0 1 1 1 1 15
5.5 224 67 0.78 0.3 0 0 0.5 0.5 1 1 17
8.5 159 35 0.89 0.4 0.83 0.67 0.5 0 1 0 19
6.5 105 52 1 0.4 0.5 0.67 1 1 1 1 19
4.5 159 77 0.89 0.7 0.83 0 0.5 0 1 1 28
2 167 37 0.78 0.6 0.33 0.67 0.5 0.5 1 1 26
5 165 32 1 0.6 0.5 1 0 0.5 1 1 15
0.5 159 36 0.78 0.2 0.67 0 0.5 0.5 1 1 26
5 119 38 0.89 0.4 1 0 0.5 0.5 1 1 16
5 176 69 0.89 0.4 0.5 0.67 0 1 1 0 24
2.5 54 21 0.89 0.5 0.67 0.33 0 0 1 0 25
5 91 26 0.89 0.3 0.17 0.67 0 0.5 0 0 22
5.5 163 54 0.89 0.4 0.83 0.33 0.5 0.5 1 1 15
3.5 124 36 0.67 0.7 0.67 0.33 0.5 1 1 0 21
3 137 42 1 0.5 0.67 0.33 0 1 0 1 22
4 121 23 0.78 0.2 0.67 0 0 1 1 0 27
0.5 153 34 0.78 0.3 0.5 0.67 0 0.5 1 1 26
6.5 148 112 0.89 0.6 1 0.33 0 1 1 1 26
4.5 221 35 0.78 0.6 0.83 0.33 0 1 1 0 22
7.5 188 47 0.89 0.2 0.83 0.33 0 1 1 1 21
5.5 149 47 0.89 0.7 1 0.67 1 0 1 1 22
4 244 37 0.33 0.2 0.67 0 0 0 1 1 20
7.5 148 109 1 1 1 0.33 1 1 0 1 21
7 92 24 0.89 0.4 0.83 0.67 0 0.5 0 0 20
4 150 20 0.89 0.4 1 1 0 1 1 1 22
5.5 153 22 0.67 0.2 0.83 0.67 0 0.5 1 0 21
2.5 94 23 0.56 0.4 0.67 0.33 0 1 1 0 8
5.5 156 32 0.89 0.4 0.67 0 0.5 1 1 0 22
3.5 132 30 0.89 0.7 1 0.67 0.5 0.5 1 1 20
2.5 161 92 1 0.2 0.67 0.67 0 0.5 1 1 24
4.5 105 43 0.78 0.6 1 1 0 0.5 1 1 17
4.5 97 55 0.78 0.3 1 1 0.5 0.5 1 1 20
4.5 151 16 0.33 0.3 0.5 0.33 0 0 1 0 23
6 131 49 0.78 0.2 0.67 0 0.5 0 0 1 20
2.5 166 71 0.89 0.5 0.83 0.67 0.5 0.5 1 1 22
5 157 43 0.89 0.7 1 0.67 0.5 1 1 0 19
0 111 29 0.78 0.6 1 0.67 0.5 0.5 1 1 15
5 145 56 0.89 0.4 1 0.67 0.5 1 1 1 20
6.5 162 46 0.89 0.6 1 0.33 0.5 1 1 1 22
5 163 19 1 0.4 1 1 0 1 1 1 17
6 59 23 0.67 0.3 0.83 0.67 0 1 0 1 14
4.5 187 59 1 0.5 0.83 0.67 0.5 0.5 1 0 24
5.5 109 30 0.89 0.2 0.5 0 0 1 1 1 17
1 90 61 0.89 0.3 0.83 0 0.5 1 0 1 23
7.5 105 7 0.89 0.5 0.17 0 0 1 1 0 25
6 83 38 0.78 0.7 0.83 1 0.5 1 0 1 16
5 116 32 0.89 0.4 1 0.67 1 0.5 0 1 18
1 42 16 0.78 0.3 1 0 0 0.5 0 1 20
5 148 19 0.78 0.2 0.67 0.67 1 1 1 1 18
6.5 155 22 1 0.5 1 0 0 0.5 0 1 23
7 125 48 0.78 0.4 1 0 0.5 0 1 1 24
4.5 116 23 1 0.6 1 0.67 1 1 1 1 23
0 128 26 0.78 0.4 0.83 1 0 1 0 0 13
8.5 138 33 0.67 0.4 0.33 0 0 0.5 1 1 20
3.5 49 9 0.33 0.2 0.33 0.33 0 0 0 0 20
7.5 96 24 1 0.9 1 0.67 0.5 1 0 1 19
3.5 164 34 1 0.8 1 0.67 1 0.5 1 1 22
6 162 48 0.78 0.8 0.83 0 0.5 1 1 0 22
1.5 99 18 0.67 0.3 1 1 0.5 1 1 0 15
9 202 43 1 0.2 0.83 0.67 0 0.5 1 1 17
3.5 186 33 0.89 0.4 0.67 0 0.5 1 1 0 19
3.5 66 28 0.89 0.2 0.83 1 0 1 0 1 20
4 183 71 0.78 0.2 0.67 0.67 0.5 1 1 0 22
6.5 214 26 1 0.1 0.83 0.67 0 1 1 1 21
7.5 188 67 0.56 0.4 0.67 1 0.5 0 1 1 21
6 104 34 0.67 0.5 1 0 0.5 0.5 0 0 16
5 177 80 0.89 0.8 0.83 0.33 0.5 1 1 0 20
5.5 126 29 0.89 0.4 0.67 0.67 0 0.5 1 0 21
3.5 76 16 0.89 0.6 0.83 0.33 0.5 0.5 0 0 20
7.5 99 59 0.89 0.5 0.83 0.67 0.5 1 0 1 23
6.5 139 32 0.78 0.3 0.67 0 0 0 1 0 18
6.5 162 43 1 0.4 0.33 0 0.5 0 1 0 16
6.5 108 38 1 0.6 0.83 0.67 0.5 0.5 0 1 17
7 159 29 0.89 0.4 1 0.33 0 0.5 1 0 24
3.5 74 36 0.44 0.3 0.83 0 0 0 0 0 13
1.5 110 32 0.78 0.8 0.83 0 1 1 1 1 19
4 96 35 0.89 0.6 0.5 0.33 1 1 0 0 20
7.5 116 21 0.67 0.3 0.5 0 0 0 0 0 22
4.5 87 29 0.78 0.5 0.83 0.67 0.5 1 0 0 19
0 97 12 0.78 0.4 1 0.33 0 1 0 1 21
3.5 127 37 0.33 0.3 0.33 0.67 0 0 0 0 15
5.5 106 37 0.89 0.7 1 0.33 0 0.5 0 1 21
5 80 47 0.89 0.2 0.67 0.33 0.5 0.5 0 1 24
4.5 74 51 0.89 0.4 0.83 1 0 1 0 0 22
2.5 91 32 0.89 0.6 1 0.67 0.5 0.5 0 0 20
7.5 133 21 0.56 0.6 0.83 0 0 1 0 0 21
7 74 13 0.67 0.6 0.83 0.67 0.5 0.5 0 1 19
0 114 14 0.67 0.4 1 0.33 0.5 1 0 1 14
4.5 140 -2 0.78 0.6 0.83 0 0 1 0 1 25
3 95 20 0.78 0.5 1 0.33 0.5 1 0 0 11
1.5 98 24 0.78 0.5 0.83 0 0 1 0 1 17
3.5 121 11 0.89 0.6 0.67 0 0 1 0 0 22
2.5 126 23 1 0.8 0.83 0.33 0.5 1 0 1 20
5.5 98 24 0.89 0.5 0.83 0.67 1 0.5 0 1 22
8 95 14 0.89 0.6 0.83 0.67 0.5 1 0 1 15
1 110 52 0.78 0.4 0.83 0.67 0.5 1 0 1 23
5 70 15 1 0.3 0.67 0.67 0.5 1 0 1 20
4.5 102 23 0.78 0.3 0.83 1 0 0.5 0 0 22
3 86 19 0.67 0.2 0 0 0 0 0 1 16
3 130 35 0.78 0.4 0.83 0 0 0.5 0 1 25
8 96 24 0.89 0.5 1 0 0 0.5 0 1 18
2.5 102 39 0.67 0.3 0.17 0 0.5 0 0 0 19
7 100 29 0.22 0.4 0.17 0 0.5 0 0 0 25
0 94 13 0.44 0.5 0.5 1 0 0 0 0 21
1 52 8 0.89 0.3 0.5 0.67 0 1 0 0 22
3.5 98 18 0.67 0.5 1 0 0 0.5 0 0 21
5.5 118 24 0.89 0.4 0.67 0.67 0 0.5 0 0 22
5.5 99 19 0.67 0.4 0.83 0.67 0 1 0 1 23
0.5 48 23 0.78 0.6 1 0 1 1 1 1 20
7.5 50 16 0.78 0.3 1 0.67 1 1 1 1 6
9 150 33 0.78 0.4 1 0.33 1 0.5 1 1 15
9.5 154 32 1 0.3 1 1 1 1 1 1 18
8.5 109 37 0.78 1 1 1 1 1 0 0 24
7 68 14 0.67 0.4 1 0 0 0.5 0 1 22
8 194 52 0.89 0.8 0.83 1 0.5 1 1 1 21
10 158 75 0.89 0.3 1 0.67 1 1 1 0 23
7 159 72 1 0.5 0.83 0.67 0 1 1 1 20
8.5 67 15 0.78 0.4 1 0 0 0.5 1 0 20
9 147 29 0.67 0.3 0.83 0.67 0 1 1 0 18
9.5 39 13 0.89 0.5 0.83 1 0 1 1 1 25
4 100 40 0.67 0.3 1 0.67 0 1 1 1 16
6 111 19 0.67 0.3 0.67 0 0 1 1 1 20
8 138 24 1 0.4 0.83 0 0 1 1 1 14
5.5 101 121 0.67 0.3 1 0 0 0.5 1 1 22
9.5 131 93 1 0.6 1 0.33 0.5 0.5 0 1 26
7.5 101 36 0.89 0.6 0.83 0.67 1 1 1 1 20
7 114 23 0.89 0.4 1 1 1 1 1 1 17
7.5 165 85 1 0.4 1 0 0 0 1 0 22
8 114 41 0.67 0.4 1 0.67 0 0.5 1 1 22
7 111 46 0.44 0.3 0.67 0.67 0.5 1 1 1 20
7 75 18 0.89 0.2 1 0.33 1 0 1 1 17
6 82 35 0.56 0.5 0.83 0.67 0 1 1 1 22
10 121 17 0.78 0.4 1 0.67 1 1 1 1 17
2.5 32 4 1 0.4 1 0.67 0 0 1 1 22
9 150 28 1 0.4 0.83 0.67 0 1 1 0 21
8 117 44 0.89 0.3 0.67 0.67 0.5 0.5 1 1 25
6 71 10 0.67 0.4 0.83 0.67 1 0.5 0 1 11
8.5 165 38 0.89 0.2 1 0.33 0.5 1 1 1 19
6 154 57 0.33 0 0 0 0 0 1 1 24
9 126 23 0.89 0.4 1 0.67 0.5 1 1 1 17
8 149 36 0.78 0.6 1 0 1 1 1 0 22
9 145 22 1 0.4 0.67 0.67 0 0.5 1 0 17
5.5 120 40 0.44 0.4 1 0 0 0.5 1 1 26
7 109 31 0.67 0.4 0.83 0 0.5 0 1 0 20
5.5 132 11 0.33 0.2 0.17 0 0.5 0 1 0 19
9 172 38 0.89 0.4 0.83 1 1 1 1 1 21
2 169 24 0.89 0.3 0.83 0 0 0.5 1 0 24
8.5 114 37 1 0.6 0.83 0.67 1 0 1 1 21
9 156 37 0.89 0.6 0.83 1 0 1 1 1 19
8.5 172 22 0.89 0.4 0.83 0 0 1 1 0 13
9 68 15 1 0.5 1 0.67 1 0.5 0 1 24
7.5 89 2 0.89 0.4 0.83 0 0.5 1 0 1 28
10 167 43 1 0.6 1 1 1 1 1 1 27
9 113 31 0.78 0.6 0.83 0.67 0.5 1 1 0 22
7.5 115 29 0.78 0.9 1 0.67 0.5 1 0 0 23
6 78 45 0.67 0.4 0.83 0.67 0.5 0 0 0 19
10.5 118 25 0.89 0.8 1 1 0.5 1 0 0 18
8.5 87 4 0.67 0.5 0.83 1 0 1 0 1 23
8 173 31 0.78 0.4 0.83 1 0 0 1 0 21
10 2 -4 0.89 0.4 1 0.67 1 0.5 1 1 22
10.5 162 66 0.89 0.7 1 1 1 0.5 0 0 17
6.5 49 61 0.78 0.4 1 0.33 1 1 0 1 15
9.5 122 32 1 0.8 1 0.67 0.5 1 0 0 21
8.5 96 31 1 0.4 1 1 1 0.5 0 1 20
7.5 100 39 1 0.3 1 0.67 0 0.5 0 0 26
5 82 19 0.67 0.5 1 0.67 0.5 1 0 0 19
8 100 31 0.89 0.8 1 0.67 1 1 0 1 28
10 115 36 1 0.4 0.83 0.33 0 0.5 0 0 21
7 141 42 1 1 1 1 0.5 0 0 1 19
7.5 165 21 0.89 0.5 1 0.67 1 1 1 1 22
7.5 165 21 0.89 0.5 1 0.67 1 1 1 1 21
9.5 110 25 0.89 0.3 1 0.33 0 1 0 1 20
6 118 32 0.89 0.3 0.83 0.33 0.5 1 1 1 19
10 158 26 0.89 0.3 0.5 0 0 1 1 0 11
7 146 28 1 0.4 0.67 0.33 0.5 0.5 0 1 17
3 49 32 0.67 0.5 1 0.33 0 1 1 0 19
6 90 41 1 0.5 0.67 0.67 0.5 1 0 0 20
7 121 29 0.89 0.4 1 0 0 0 0 0 17
10 155 33 0.89 0.7 1 1 0.5 0 1 1 21
7 104 17 0.89 0.5 0.5 0.33 0 0.5 0 0 21
3.5 147 13 0.89 0.4 0.67 0.33 1 0 0 1 12
8 110 32 1 0.7 0.67 1 0 1 0 0 23
10 108 30 1 0.7 0.67 1 0 1 0 0 22
5.5 113 34 1 0.7 0.67 1 0 1 0 0 22
6 115 59 0.89 0.7 0.67 1 0 1 0 0 21
6.5 61 13 0.89 0.7 0.67 0 0 0 0 1 20
6.5 60 23 0.89 0.7 1 0.67 0.5 1 0 1 18
8.5 109 10 0.33 0.1 0.67 0.33 0.5 0 0 1 21
4 68 5 0.67 0.2 0.67 0.67 0.5 1 0 1 24
9.5 111 31 0.56 0.3 0.33 0.33 0 1 0 0 22
8 77 19 0.44 0.6 0.83 0.33 0 0.5 0 0 20
8.5 73 32 1 0.8 1 1 1 1 0 1 17
5.5 151 30 0.89 0.8 1 0.33 0.5 0.5 1 0 19
7 89 25 0.33 0 0.17 0 0 0 0 0 16
9 78 48 0.67 0.3 0.67 0.33 0 1 0 0 19
8 110 35 0.67 0.6 0.83 0.33 0.5 1 0 0 23
10 220 67 1 0.5 0.83 0.67 0 1 1 1 8
8 65 15 0.78 0.7 1 0.33 0 0.5 0 1 22
6 141 22 0.67 0.3 0.83 0 0.5 1 1 0 23
8 117 18 1 0.3 1 0.67 0 0 0 0 15
5 122 33 0.78 0.4 1 0.67 0 0.5 1 1 17
9 63 46 0.89 0.4 0.83 1 0 1 0 0 21
4.5 44 24 0.89 0.1 0.83 0 0 1 1 1 25
8.5 52 14 0.89 0.5 1 0.67 0 1 0 1 18
9.5 131 12 0 0 0 0 0 0 0 0 20
8.5 101 38 0.67 0.4 1 0.33 0.5 0 0 1 21
7.5 42 12 1 0.6 0.83 0.67 1 0.5 0 1 21
7.5 152 28 1 0.4 1 0.33 0.5 1 1 1 24
5 107 41 0.67 0.1 0.33 0 0.5 1 1 0 22
7 77 12 0.89 0.3 0.83 0 0 1 0 0 22
8 154 31 0.89 0.7 0.83 0.67 0 1 1 0 23
5.5 103 33 0.56 0.3 0.17 0 0 1 1 1 17
8.5 96 34 0.67 0.5 0.83 0.33 0.5 0 0 1 15
9.5 175 21 1 0.3 0.83 0.67 1 1 1 1 22
7 57 20 1 0.6 0.67 0.67 0.5 1 0 1 19
8 112 44 1 0.9 1 1 0 1 0 0 18
8.5 143 52 0.67 0.4 0.83 0 0.5 1 1 0 21
3.5 49 7 0.44 0.3 1 0 0.5 0.5 0 0 20
6.5 110 29 0.89 0.9 1 0.67 1 1 1 1 19
6.5 131 11 0.44 0.5 1 0 0.5 0 1 1 19
10.5 167 26 0.56 0.3 1 1 0.5 0.5 1 0 16
8.5 56 24 0.89 0.6 0.83 0.67 0 0.5 0 0 18
8 137 7 0.67 0.2 1 0.33 0 0.5 1 0 23
10 86 60 0.89 0.4 0.83 1 0.5 1 0 1 22
10 121 13 1 0.5 0.83 0.67 0.5 0.5 1 1 23
9.5 149 20 0.78 0.4 0.83 0.67 0 0.5 1 0 20
9 168 52 0.44 0 0 0 0 0 1 0 24
10 140 28 0.89 0.2 1 0.33 0.5 1 1 0 25
7.5 88 25 0.89 0.5 1 0.67 0.5 1 0 1 25
4.5 168 39 0.89 0.3 1 0.67 0 0.5 1 1 20
4.5 94 9 0.44 0 0 0 0 0 1 1 23
0.5 51 19 1 0.5 0.83 1 0 1 1 1 21
6.5 48 13 0.89 0.6 0.83 0.33 0 1 0 0 23
4.5 145 60 0.67 0.3 0.83 0 0.5 0.5 1 1 23
5.5 66 19 0.33 0 0 0 0 0 1 1 11
5 85 34 0.78 0.3 0.67 0 0.5 0 0 1 21
6 109 14 0.89 0.5 1 0.67 0.5 1 1 0 27
4 63 17 0.78 0.4 0.67 0 0 1 0 0 19
8 102 45 0.78 0.5 0.83 0.67 0 0.5 0 1 21
10.5 162 66 0.89 0.7 1 1 1 0.5 0 0 16
6.5 86 48 0.78 0.8 1 0.67 0.5 1 0 1 21
8 114 29 0.78 0.6 1 0.33 0.5 1 0 1 22
8.5 164 -2 0.67 0.4 0.83 0.33 0 0.5 1 0 16
5.5 119 51 0.89 0.5 0.83 0.33 0.5 0 1 1 18
7 126 2 0.89 0.5 1 0 0.5 1 1 0 23
5 132 24 0.78 0.3 1 0.33 0 1 1 1 24
3.5 142 40 1 0.6 1 0 0.5 1 1 1 20
5 83 20 1 0.3 0.67 0.67 0 0.5 1 0 20
9 94 19 0.78 0.6 0.83 1 0.5 0.5 0 1 18
8.5 81 16 0.78 0.3 0.33 0.33 0 1 0 0 4
5 166 20 0.89 0.7 1 0.67 1 1 1 1 14
9.5 110 40 0.89 0.7 1 1 0 1 0 0 22
3 64 27 0.67 0.6 0.67 1 0.5 1 0 1 17
1.5 93 25 1 0.5 1 0.33 0.5 0 1 0 23
6 104 49 0.67 0.5 0.83 0.33 0 0.5 0 0 20
0.5 105 39 0.56 0.4 0.67 0 0 1 0 1 18
6.5 49 61 0.78 0.4 1 0.33 1 1 0 1 19
7.5 88 19 1 0.7 1 1 0 1 0 0 20
4.5 95 67 0.67 0.2 0.17 0 0.5 0 0 1 15
8 102 45 0.78 0.5 0.83 0.67 0 0.5 0 1 24
9 99 30 0.56 0.4 0.83 0.67 0.5 0 0 0 21
7.5 63 8 1 0.2 1 0.67 1 1 0 1 19
8.5 76 19 0.89 0.5 0.67 0.67 0 0 0 0 19
7 109 52 0.44 0.4 0.5 0 0 1 0 0 27
9.5 117 22 1 0.7 0.67 1 1 1 0 1 23
6.5 57 17 0.89 0.6 0.83 0.67 1 0 0 1 23
9.5 120 33 0.78 0.4 0.83 0 0 0 0 0 20
6 73 34 0.89 0.5 1 0.67 1 1 0 1 17
8 91 22 0.11 0 0.17 0 0 0 0 0 21
9.5 108 30 0.89 0.7 1 0.67 0.5 1 0 0 23
8 105 25 0.89 0.4 0.67 0.67 0 1 0 1 22
8 117 38 1 0.5 0.67 1 0 1 1 0 16
9 119 26 0.89 0.6 0.83 0.67 0 0.5 0 0 20
5 31 13 1 0.8 0.5 0.67 0.5 0.5 0 1 16

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
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 (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
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)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(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 - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,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*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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,'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')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')