Calculate the forecast error variance for a VAR out to a specified horizon
fev.RdCalculate the forecast error variance for a VAR out to a specified horizon
Examples
x <- svars::USA
v <- vars::VAR(x, p = 2)
mvar <- id_fevdtd(v, "pi", 4:10)
fev(mvar)
#> $fev
#> h impulse response fev
#> 1 1 Main x 0.004595113
#> 2 2 Main x 0.014520166
#> 3 3 Main x 0.019266752
#> 4 4 Main x 0.020059480
#> 5 5 Main x 0.020671316
#> 6 6 Main x 0.027137434
#> 7 7 Main x 0.045556585
#> 8 8 Main x 0.080890511
#> 9 9 Main x 0.136434821
#> 10 10 Main x 0.213670783
#> 11 11 Main x 0.312394627
#> 12 12 Main x 0.430984605
#> 13 13 Main x 0.566732614
#> 14 14 Main x 0.716186771
#> 15 15 Main x 0.875473447
#> 16 16 Main x 1.040580261
#> 17 17 Main x 1.207590895
#> 18 18 Main x 1.372868757
#> 19 19 Main x 1.533190663
#> 20 20 Main x 1.685834304
#> 21 1 Orth_2 x 0.508244017
#> 22 2 Orth_2 x 1.162171224
#> 23 3 Orth_2 x 1.713622950
#> 24 4 Orth_2 x 2.087258063
#> 25 5 Orth_2 x 2.308804924
#> 26 6 Orth_2 x 2.426734453
#> 27 7 Orth_2 x 2.482615172
#> 28 8 Orth_2 x 2.505097353
#> 29 9 Orth_2 x 2.511767866
#> 30 10 Orth_2 x 2.512580849
#> 31 11 Orth_2 x 2.512736780
#> 32 12 Orth_2 x 2.514658889
#> 33 13 Orth_2 x 2.519225947
#> 34 14 Orth_2 x 2.526500403
#> 35 15 Orth_2 x 2.536143487
#> 36 16 Orth_2 x 2.547645762
#> 37 17 Orth_2 x 2.560452239
#> 38 18 Orth_2 x 2.574028399
#> 39 19 Orth_2 x 2.587893458
#> 40 20 Orth_2 x 2.601635504
#> 41 1 Orth_3 x 0.007792541
#> 42 2 Orth_3 x 0.009796929
#> 43 3 Orth_3 x 0.018413679
#> 44 4 Orth_3 x 0.044445679
#> 45 5 Orth_3 x 0.093862369
#> 46 6 Orth_3 x 0.166538604
#> 47 7 Orth_3 x 0.257176998
#> 48 8 Orth_3 x 0.358516886
#> 49 9 Orth_3 x 0.463345230
#> 50 10 Orth_3 x 0.565656760
#> 51 11 Orth_3 x 0.661039172
#> 52 12 Orth_3 x 0.746643969
#> 53 13 Orth_3 x 0.820959228
#> 54 14 Orth_3 x 0.883521954
#> 55 15 Orth_3 x 0.934638030
#> 56 16 Orth_3 x 0.975138912
#> 57 17 Orth_3 x 1.006183262
#> 58 18 Orth_3 x 1.029101925
#> 59 19 Orth_3 x 1.045280926
#> 60 20 Orth_3 x 1.056076220
#> 61 1 Main pi 1.132451873
#> 62 2 Main pi 1.644120454
#> 63 3 Main pi 2.219943937
#> 64 4 Main pi 2.714314473
#> 65 5 Main pi 3.162254522
#> 66 6 Main pi 3.557117371
#> 67 7 Main pi 3.901892679
#> 68 8 Main pi 4.198292387
#> 69 9 Main pi 4.449121494
#> 70 10 Main pi 4.657701697
#> 71 11 Main pi 4.827846644
#> 72 12 Main pi 4.963689973
#> 73 13 Main pi 5.069534363
#> 74 14 Main pi 5.149703576
#> 75 15 Main pi 5.208411936
#> 76 16 Main pi 5.249654856
#> 77 17 Main pi 5.277122943
#> 78 18 Main pi 5.294139975
#> 79 19 Main pi 5.303623666
#> 80 20 Main pi 5.308067175
#> 81 1 Orth_2 pi 0.033645101
#> 82 2 Orth_2 pi 0.039663212
#> 83 3 Orth_2 pi 0.039695982
#> 84 4 Orth_2 pi 0.042832556
#> 85 5 Orth_2 pi 0.051488343
#> 86 6 Orth_2 pi 0.064847267
#> 87 7 Orth_2 pi 0.081315404
#> 88 8 Orth_2 pi 0.099147182
#> 89 9 Orth_2 pi 0.116916255
#> 90 10 Orth_2 pi 0.133593209
#> 91 11 Orth_2 pi 0.148530071
#> 92 12 Orth_2 pi 0.161391075
#> 93 13 Orth_2 pi 0.172076323
#> 94 14 Orth_2 pi 0.180652039
#> 95 15 Orth_2 pi 0.187293082
#> 96 16 Orth_2 pi 0.192238071
#> 97 17 Orth_2 pi 0.195755715
#> 98 18 Orth_2 pi 0.198120407
#> 99 19 Orth_2 pi 0.199595222
#> 100 20 Orth_2 pi 0.200420695
#> 101 1 Orth_3 pi 0.012420848
#> 102 2 Orth_3 pi 0.020919450
#> 103 3 Orth_3 pi 0.021881981
#> 104 4 Orth_3 pi 0.023702066
#> 105 5 Orth_3 pi 0.024371236
#> 106 6 Orth_3 pi 0.024479501
#> 107 7 Orth_3 pi 0.024552304
#> 108 8 Orth_3 pi 0.025333206
#> 109 9 Orth_3 pi 0.027556213
#> 110 10 Orth_3 pi 0.031823753
#> 111 11 Orth_3 pi 0.038547158
#> 112 12 Orth_3 pi 0.047924269
#> 113 13 Orth_3 pi 0.059949809
#> 114 14 Orth_3 pi 0.074443544
#> 115 15 Orth_3 pi 0.091088120
#> 116 16 Orth_3 pi 0.109469770
#> 117 17 Orth_3 pi 0.129117629
#> 118 18 Orth_3 pi 0.149538954
#> 119 19 Orth_3 pi 0.170248816
#> 120 20 Orth_3 pi 0.190793646
#> 121 1 Main i 0.074521015
#> 122 2 Main i 0.212582985
#> 123 3 Main i 0.453779975
#> 124 4 Main i 0.761150876
#> 125 5 Main i 1.119750825
#> 126 6 Main i 1.508162374
#> 127 7 Main i 1.909679861
#> 128 8 Main i 2.310058004
#> 129 9 Main i 2.698069031
#> 130 10 Main i 3.065125688
#> 131 11 Main i 3.405034487
#> 132 12 Main i 3.713693948
#> 133 13 Main i 3.988803036
#> 134 14 Main i 4.229575433
#> 135 15 Main i 4.436466263
#> 136 16 Main i 4.610914236
#> 137 17 Main i 4.755102558
#> 138 18 Main i 4.871741426
#> 139 19 Main i 4.963874423
#> 140 20 Main i 5.034710353
#> 141 1 Orth_2 i 0.121163005
#> 142 2 Orth_2 i 0.513572738
#> 143 3 Orth_2 i 0.948654515
#> 144 4 Orth_2 i 1.323993165
#> 145 5 Orth_2 i 1.615854344
#> 146 6 Orth_2 i 1.833886691
#> 147 7 Orth_2 i 1.995836785
#> 148 8 Orth_2 i 2.117534781
#> 149 9 Orth_2 i 2.210820796
#> 150 10 Orth_2 i 2.283904229
#> 151 11 Orth_2 i 2.342307067
#> 152 12 Orth_2 i 2.389714385
#> 153 13 Orth_2 i 2.428604126
#> 154 14 Orth_2 i 2.460676975
#> 155 15 Orth_2 i 2.487139339
#> 156 16 Orth_2 i 2.508885845
#> 157 17 Orth_2 i 2.526615118
#> 158 18 Orth_2 i 2.540901762
#> 159 19 Orth_2 i 2.552239821
#> 160 20 Orth_2 i 2.561067878
#> 161 1 Orth_3 i 0.648255072
#> 162 2 Orth_3 i 1.279944107
#> 163 3 Orth_3 i 1.807093155
#> 164 4 Orth_3 i 2.211015492
#> 165 5 Orth_3 i 2.498479368
#> 166 6 Orth_3 i 2.695886973
#> 167 7 Orth_3 i 2.826972035
#> 168 8 Orth_3 i 2.911203149
#> 169 9 Orth_3 i 2.963115234
#> 170 10 Orth_3 i 2.993284145
#> 171 11 Orth_3 i 3.009302927
#> 172 12 Orth_3 i 3.016601601
#> 173 13 Orth_3 i 3.019048060
#> 174 14 Orth_3 i 3.019372152
#> 175 15 Orth_3 i 3.019461485
#> 176 16 Orth_3 i 3.020570258
#> 177 17 Orth_3 i 3.023470756
#> 178 18 Orth_3 i 3.028567295
#> 179 19 Orth_3 i 3.035985201
#> 180 20 Orth_3 i 3.045642533
#>
#> attr(,"class")
#> [1] "fevdfev"