Forecast error variance decomposition.
fevd.RdAlias for the function from svars::fevd.svars, so that the shock names are correct.
Value
Matrix of forecast error variance decomposition in frequency domain Indexed: frequencies, variables, shocks
Examples
x <- svars::USA
v <- vars::VAR(x, p = 2)
mvar <- id_fevdtd(v, "pi", 4:10)
vars::fevd(mvar)
#> $fevd
#> h impulse response fevd
#> 1 1 Main x 0.8826034
#> 2 2 Main x 1.2237934
#> 3 3 Main x 1.1001379
#> 4 4 Main x 0.9322346
#> 5 5 Main x 0.8530098
#> 6 6 Main x 1.0356177
#> 7 7 Main x 1.6355792
#> 8 8 Main x 2.7471686
#> 9 9 Main x 4.3847893
#> 10 10 Main x 6.4907876
#> 11 1 Orth_2 x 97.6206492
#> 12 2 Orth_2 x 97.9504986
#> 13 3 Orth_2 x 97.8484350
#> 14 4 Orth_2 x 97.0022185
#> 15 5 Orth_2 x 95.2737234
#> 16 6 Orth_2 x 92.6089428
#> 17 7 Orth_2 x 89.1312144
#> 18 8 Orth_2 x 85.0770355
#> 19 9 Orth_2 x 80.7240619
#> 20 10 Orth_2 x 76.3259651
#> 21 1 Orth_3 x 1.4967474
#> 22 2 Orth_3 x 0.8257080
#> 23 3 Orth_3 x 1.0514271
#> 24 4 Orth_3 x 2.0655469
#> 25 5 Orth_3 x 3.8732668
#> 26 6 Orth_3 x 6.3554395
#> 27 7 Orth_3 x 9.2332063
#> 28 8 Orth_3 x 12.1757958
#> 29 9 Orth_3 x 14.8911488
#> 30 10 Orth_3 x 17.1832473
#> 31 1 Main pi 96.0911963
#> 32 2 Main pi 96.4461459
#> 33 3 Main pi 97.3010137
#> 34 4 Main pi 97.6073990
#> 35 5 Main pi 97.6572914
#> 36 6 Main pi 97.5503048
#> 37 7 Main pi 97.3584322
#> 38 8 Main pi 97.1203578
#> 39 9 Main pi 96.8549143
#> 40 10 Main pi 96.5703319
#> 41 1 Orth_2 pi 2.8548657
#> 42 2 Orth_2 pi 2.3266932
#> 43 3 Orth_2 pi 1.7398905
#> 44 4 Orth_2 pi 1.5402690
#> 45 5 Orth_2 pi 1.5900719
#> 46 6 Orth_2 pi 1.7783700
#> 47 7 Orth_2 pi 2.0289488
#> 48 8 Orth_2 pi 2.2936015
#> 49 9 Orth_2 pi 2.5452022
#> 50 10 Orth_2 pi 2.7698512
#> 51 1 Orth_3 pi 1.0539380
#> 52 2 Orth_3 pi 1.2271609
#> 53 3 Orth_3 pi 0.9590958
#> 54 4 Orth_3 pi 0.8523320
#> 55 5 Orth_3 pi 0.7526367
#> 56 6 Orth_3 pi 0.6713253
#> 57 7 Orth_3 pi 0.6126191
#> 58 8 Orth_3 pi 0.5860407
#> 59 9 Orth_3 pi 0.5998835
#> 60 10 Orth_3 pi 0.6598169
#> 61 1 Main i 8.8301414
#> 62 2 Main i 10.5968298
#> 63 3 Main i 14.1385283
#> 64 4 Main i 17.7170068
#> 65 5 Main i 21.3934417
#> 66 6 Main i 24.9781111
#> 67 7 Main i 28.3651403
#> 68 8 Main i 31.4773435
#> 69 9 Main i 34.2742289
#> 70 10 Main i 36.7419120
#> 71 1 Orth_2 i 14.3568423
#> 72 2 Orth_2 i 25.6005574
#> 73 3 Orth_2 i 29.5574496
#> 74 4 Orth_2 i 30.8180633
#> 75 5 Orth_2 i 30.8717663
#> 76 6 Orth_2 i 30.3727413
#> 77 7 Orth_2 i 29.6448591
#> 78 8 Orth_2 i 28.8539810
#> 79 9 Orth_2 i 28.0845957
#> 80 10 Orth_2 i 27.3773465
#> 81 1 Orth_3 i 76.8130162
#> 82 2 Orth_3 i 63.8026128
#> 83 3 Orth_3 i 56.3040221
#> 84 4 Orth_3 i 51.4649299
#> 85 5 Orth_3 i 47.7347920
#> 86 6 Orth_3 i 44.6491476
#> 87 7 Orth_3 i 41.9900006
#> 88 8 Orth_3 i 39.6686756
#> 89 9 Orth_3 i 37.6411754
#> 90 10 Orth_3 i 35.8807415
#>
#> attr(,"class")
#> [1] "fevdfevd"