etf <- etf_vix[1:55, 1:3]
# Split-------------------------------
h <- 5
etf_eval <- divide_ts(etf, h)
etf_train <- etf_eval$train
etf_test <- etf_eval$test
var_bayes()
and vhar_bayes()
fit BVAR and
BVHAR each with various priors.
y
: Multivariate time series data. It should be data
frame or matrix, which means that every column is numeric. Each column
indicates variable, i.e. it sould be wide format.p
or har
: VAR lag, or order of VHARnum_chains
: Number of chains
num_iter
: Total number of iterationsnum_burn
: Number of burn-inthinning
: Thinningbayes_spec
: Output of set_ssvs()
set_bvar()
set_bvhar()
and
set_weight_bvhar()
lambda = set_lambda()
set_ssvs()
set_horseshoe()
set_ng()
set_dl()
cov_spec
: Covariance prior specification. Use
set_ldlt()
for homoskedastic model.include_mean = TRUE
: By default, you include the
constant term in the model.minnesota = c("no", "short", "longrun")
: Minnesota-type
shrinkage.verbose = FALSE
: Progress barnum_thread
: Number of thread for OpenMP
(fit_ssvs <- vhar_bayes(etf_train, num_chains = 1, num_iter = 20, bayes_spec = set_ssvs(), cov_spec = set_ldlt(), include_mean = FALSE, minnesota = "longrun"))
#> Call:
#> vhar_bayes(y = etf_train, num_chains = 1, num_iter = 20, bayes_spec = set_ssvs(),
#> cov_spec = set_ldlt(), include_mean = FALSE, minnesota = "longrun")
#>
#> BVHAR with SSVS prior
#> Fitted by Gibbs sampling
#> Total number of iteration: 20
#> Number of burn-in: 10
#> ====================================================
#>
#> Parameter Record:
#> # A draws_df: 10 iterations, 1 chains, and 90 variables
#> phi[1] phi[2] phi[3] phi[4] phi[5] phi[6] phi[7] phi[8]
#> 1 0.0429 0.1032 0.0751 -1.065 0.2814 -0.299 1.9280 1.4308
#> 2 0.3587 -0.2091 0.2117 -0.147 0.2603 -0.212 -0.0805 -0.1194
#> 3 -0.0920 -0.0893 0.1198 0.065 -0.2134 -0.199 -0.0660 -0.0336
#> 4 0.2715 -0.2033 0.1803 -0.724 0.0192 -0.386 1.4648 0.2072
#> 5 0.0187 -0.1021 0.1732 -0.953 0.0856 -0.625 1.3928 0.7355
#> 6 0.1987 -0.1968 0.2466 -0.956 0.1447 -0.558 1.4975 0.4995
#> 7 0.1982 -0.2029 0.0768 -0.831 0.3321 -0.356 1.4978 0.5742
#> 8 -0.1002 -0.3761 0.0268 -0.799 0.1613 -0.499 1.1633 -0.0106
#> 9 0.1921 -0.4328 -0.1678 -0.619 0.0504 -0.333 0.6136 0.0106
#> 10 0.2367 -0.2305 -0.1977 -0.059 -0.1013 -0.316 -0.0623 -0.1687
#> # ... with 82 more variables
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
autoplot()
for the fit (bvharsp
object)
provides coefficients heatmap. There is type
argument, and
the default type = "coef"
draws the heatmap.
bayes_spec
is the initial specification by
set_horseshoe()
. Others are the same.
(fit_hs <- vhar_bayes(etf_train, num_chains = 2, num_iter = 20, bayes_spec = set_horseshoe(), cov_spec = set_ldlt(), include_mean = FALSE, minnesota = "longrun"))
#> Call:
#> vhar_bayes(y = etf_train, num_chains = 2, num_iter = 20, bayes_spec = set_horseshoe(),
#> cov_spec = set_ldlt(), include_mean = FALSE, minnesota = "longrun")
#>
#> BVHAR with Horseshoe prior
#> Fitted by Gibbs sampling
#> Number of chains: 2
#> Total number of iteration: 20
#> Number of burn-in: 10
#> ====================================================
#>
#> Parameter Record:
#> # A draws_df: 10 iterations, 2 chains, and 124 variables
#> phi[1] phi[2] phi[3] phi[4] phi[5] phi[6] phi[7] phi[8]
#> 1 -0.00950 -0.6781 1.0408 0.099660 -0.00867 -0.00638 0.2857 1.2204
#> 2 -0.00484 -0.2053 1.8099 -0.010360 0.21034 0.01308 0.3589 0.1133
#> 3 -0.00853 -0.2376 0.8261 0.000923 0.10468 0.43175 0.4615 0.4400
#> 4 -0.00542 -0.0203 0.0712 0.003396 0.34441 0.17688 0.0340 0.2259
#> 5 -0.00183 -0.0223 -0.0371 -0.002231 2.19899 -0.03177 0.0426 0.0427
#> 6 0.00448 -0.1139 0.0842 -0.000445 2.54582 -0.02232 0.3654 0.1101
#> 7 -0.02708 0.0637 0.6941 0.000747 -0.10755 -0.04615 0.2165 0.1768
#> 8 0.36783 -0.0294 -0.3218 0.000111 1.08201 0.05984 0.4627 0.0699
#> 9 -0.03679 -0.2504 -0.0354 0.000336 0.97651 0.08571 0.2866 -0.1106
#> 10 0.04780 -0.2722 -0.0107 0.000154 0.35662 0.00911 0.0791 -0.0579
#> # ... with 10 more draws, and 116 more variables
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
(fit_mn <- vhar_bayes(etf_train, num_chains = 2, num_iter = 20, bayes_spec = set_bvhar(lambda = set_lambda()), cov_spec = set_ldlt(), include_mean = FALSE, minnesota = "longrun"))
#> Call:
#> vhar_bayes(y = etf_train, num_chains = 2, num_iter = 20, bayes_spec = set_bvhar(lambda = set_lambda()),
#> cov_spec = set_ldlt(), include_mean = FALSE, minnesota = "longrun")
#>
#> BVHAR with MN_Hierarchical prior
#> Fitted by Gibbs sampling
#> Number of chains: 2
#> Total number of iteration: 20
#> Number of burn-in: 10
#> ====================================================
#>
#> Parameter Record:
#> # A draws_df: 10 iterations, 2 chains, and 63 variables
#> phi[1] phi[2] phi[3] phi[4] phi[5] phi[6] phi[7] phi[8]
#> 1 0.0960 -0.06454 0.0898 0.149039 0.15948 1.286 0.3296 0.17588
#> 2 0.4650 -0.10282 -0.0732 0.359101 0.01651 0.843 0.2547 -0.00466
#> 3 0.4987 -0.35645 0.1462 -0.109077 0.09793 1.257 0.2210 0.01337
#> 4 0.0256 -0.11528 0.1021 0.058250 0.22298 0.909 0.4084 0.13241
#> 5 0.3072 -0.25352 0.2687 0.278917 -0.02073 1.177 0.3303 -0.33796
#> 6 0.1886 -0.16339 0.2272 0.168824 0.02504 0.963 0.0847 -0.30628
#> 7 0.2001 -0.16268 0.0189 0.439802 0.42224 1.013 0.6046 0.05434
#> 8 0.3528 -0.00251 0.3827 0.339542 -0.00989 0.545 -0.3931 -0.44756
#> 9 0.3011 -0.22574 -0.0788 -0.000334 0.22362 0.342 0.4450 -0.12164
#> 10 0.1936 -0.14646 0.0649 0.072367 0.24439 0.686 0.2699 -0.00731
#> # ... with 10 more draws, and 55 more variables
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
(fit_ng <- vhar_bayes(etf_train, num_chains = 2, num_iter = 20, bayes_spec = set_ng(), cov_spec = set_ldlt(), include_mean = FALSE, minnesota = "longrun"))
#> Call:
#> vhar_bayes(y = etf_train, num_chains = 2, num_iter = 20, bayes_spec = set_ng(),
#> cov_spec = set_ldlt(), include_mean = FALSE, minnesota = "longrun")
#>
#> BVHAR with NG prior
#> Fitted by Metropolis-within-Gibbs
#> Number of chains: 2
#> Total number of iteration: 20
#> Number of burn-in: 10
#> ====================================================
#>
#> Parameter Record:
#> # A draws_df: 10 iterations, 2 chains, and 97 variables
#> phi[1] phi[2] phi[3] phi[4] phi[5] phi[6] phi[7]
#> 1 0.0580 -0.04671 0.04265 0.00618 0.06183 -0.00638 0.00719
#> 2 -0.0241 -0.00374 0.08118 0.00090 -0.00117 0.01466 0.00972
#> 3 0.0215 -0.02203 0.08263 0.00032 0.00677 0.03121 0.02480
#> 4 0.0561 -0.09879 0.05613 -0.00130 0.00451 0.19240 0.47467
#> 5 0.2193 -0.05864 0.01103 0.00319 0.14916 0.84141 0.66151
#> 6 0.3382 -0.05972 -0.00619 -0.00962 -0.11705 1.06522 0.22307
#> 7 0.2188 -0.08351 -0.10956 0.03056 0.39904 1.57796 1.88983
#> 8 0.4294 -0.03667 0.11182 -0.03674 0.01666 0.83532 -0.53781
#> 9 0.5016 -0.14089 0.01857 -0.00409 0.02095 0.87700 -0.51530
#> 10 0.1740 -0.05936 0.08638 -0.00131 -0.25196 0.34172 0.29114
#> phi[8]
#> 1 -0.02518
#> 2 -0.06328
#> 3 0.25823
#> 4 -0.03480
#> 5 -0.08484
#> 6 0.23694
#> 7 -0.05653
#> 8 0.00358
#> 9 -0.00128
#> 10 -0.00449
#> # ... with 10 more draws, and 89 more variables
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
(fit_dl <- vhar_bayes(etf_train, num_chains = 2, num_iter = 20, bayes_spec = set_dl(), cov_spec = set_ldlt(), include_mean = FALSE, minnesota = "longrun"))
#> Call:
#> vhar_bayes(y = etf_train, num_chains = 2, num_iter = 20, bayes_spec = set_dl(),
#> cov_spec = set_ldlt(), include_mean = FALSE, minnesota = "longrun")
#>
#> BVHAR with DL prior
#> Fitted by Gibbs sampling
#> Number of chains: 2
#> Total number of iteration: 20
#> Number of burn-in: 10
#> ====================================================
#>
#> Parameter Record:
#> # A draws_df: 10 iterations, 2 chains, and 91 variables
#> phi[1] phi[2] phi[3] phi[4] phi[5] phi[6] phi[7] phi[8]
#> 1 0.0238 -0.1570 0.13196 1.0106 0.0780 0.156 0.4260 0.2456
#> 2 0.0877 -0.3112 0.01248 0.5559 0.1224 0.836 -0.1151 0.0325
#> 3 0.1909 -0.4841 -0.11611 0.8619 0.0974 0.983 0.3137 -0.0922
#> 4 0.3497 -0.2444 0.26508 0.7810 0.1025 0.648 0.0853 0.0398
#> 5 0.2307 -0.2907 0.23740 0.5146 -0.2804 1.035 -0.1117 -0.0889
#> 6 0.1309 -0.2299 -0.32532 -0.0678 0.5573 1.011 -0.2019 -0.1911
#> 7 0.0915 -0.3254 -0.13914 0.0168 0.2229 0.859 -0.5806 -0.4391
#> 8 0.0854 0.0357 0.00659 -0.0518 -0.3399 0.612 -0.5571 -0.2342
#> 9 -0.0218 0.0390 0.03880 0.2015 0.0897 0.802 0.0890 -0.0269
#> 10 0.1817 -0.1016 0.48692 0.1310 -0.1772 0.914 0.3551 -0.0416
#> # ... with 10 more draws, and 83 more variables
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
autoplot()
also provides Bayesian visualization.
type = "trace"
gives MCMC trace plot.
type = "dens"
draws MCMC density plot. If specifying
additional argument facet_args = list(dir = "v")
of
bayesplot
, you can see plot as the same format with
coefficient matrix.