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etf <- etf_vix[1:100, 1:3]
# Split-------------------------------
h <- 5
etf_eval <- divide_ts(etf, h)
etf_train <- etf_eval$train
etf_test <- etf_eval$test
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.har
: Order of VHARnum_chains
: Number of chains
num_iter
: Total number of iterationsnum_burn
: Number of burn-inthinning
: Thinningbayes_spec
: Output of set_ssvs()
choose_ssvs()
.init_spec
: Gibbs sampler initialization by
init_ssvs()
.
init_ssvs(type = "auto")
uses OLS.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 <- bvhar_ssvs(etf_train, num_chains = 1, num_iter = 20, include_mean = FALSE, minnesota = "longrun"))
#> Call:
#> bvhar_ssvs(y = etf_train, num_chains = 1, num_iter = 20, 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 63 variables
#> phi[1] phi[2] phi[3] phi[4] phi[5] phi[6] phi[7] phi[8]
#> 1 0.368 -0.00254 0.217 -0.00659 0.1339 0.04204 0.01909 0.01827
#> 2 0.468 0.01488 0.146 0.01540 0.2228 0.02630 -0.07248 -0.00927
#> 3 0.609 0.00387 0.172 0.00145 0.1130 -0.00280 -0.04074 -0.00871
#> 4 0.473 0.01275 0.235 -0.03514 0.0814 -0.00769 -0.00534 0.03317
#> 5 0.525 0.01106 0.209 -0.02084 0.1746 -0.01760 -0.01623 -0.02526
#> 6 0.510 -0.01942 0.268 0.01555 0.1615 -0.01459 -0.10195 0.00161
#> 7 0.519 0.00428 0.218 0.00403 0.1053 0.00976 -0.06733 0.04154
#> 8 0.596 -0.01144 0.143 -0.00398 0.1405 0.01432 0.02307 -0.00685
#> 9 0.486 0.00996 0.123 0.02278 0.1969 0.00884 -0.05706 -0.01282
#> 10 0.480 -0.02574 0.270 -0.03183 0.1549 -0.02135 -0.04120 0.02119
#> # ... with 55 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 <- bvhar_horseshoe(etf_train, num_chains = 2, num_iter = 20, include_mean = FALSE, minnesota = "longrun"))
#> Call:
#> bvhar_horseshoe(y = etf_train, num_chains = 2, num_iter = 20,
#> include_mean = FALSE, minnesota = "longrun")
#>
#> BVHAR with Horseshoe Prior
#> Fitted by blocked sampling
#> Number of chains: 2
#> Total number of iteration: 20
#> Number of burn-in: 10
#> ====================================================
#>
#> # A draws_df: 10 iterations, 2 chains, and 61 variables
#> phi[1] phi[2] phi[3] phi[4] phi[5] phi[6] phi[7]
#> 1 5.18e-08 1.81e-06 1.51e-04 1.33e-02 4.65e-07 1.08e-03 2.10e-04
#> 2 1.32e-08 2.41e-08 6.55e-06 5.07e-03 2.99e-08 1.51e-03 4.55e-05
#> 3 5.90e-09 3.86e-09 6.53e-07 1.33e-02 8.84e-10 1.30e-06 1.77e-06
#> 4 3.21e-09 2.17e-08 2.52e-07 1.71e-03 1.95e-09 2.25e-05 9.13e-07
#> 5 9.58e-09 2.64e-10 1.61e-06 1.08e-03 4.20e-11 8.90e-07 1.26e-07
#> 6 6.16e-09 3.09e-12 1.42e-05 3.68e-02 3.04e-13 1.30e-05 1.67e-06
#> 7 1.07e-08 -2.40e-14 8.60e-07 4.59e-02 5.47e-13 4.64e-07 7.17e-07
#> 8 3.51e-09 6.67e-13 6.98e-07 7.03e-04 -5.61e-13 2.17e-06 1.95e-08
#> 9 6.72e-12 2.13e-13 2.06e-08 9.49e-05 -2.28e-13 1.77e-07 1.68e-08
#> 10 7.99e-13 5.21e-14 1.26e-09 7.96e-05 1.43e-11 3.26e-07 4.42e-10
#> phi[8]
#> 1 2.08e-07
#> 2 6.77e-08
#> 3 8.60e-09
#> 4 2.25e-08
#> 5 1.31e-08
#> 6 3.40e-08
#> 7 3.84e-09
#> 8 1.32e-09
#> 9 3.17e-10
#> 10 2.86e-11
#> # ... with 10 more draws, and 53 more variables
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
bvhar_sv()
fits VHAR-SV with shrinkage priors.
bayes_spec
sv_spec
: prior settings for SV,
set_sv()
(fit_ssvs_sv <- bvhar_sv(etf_train, num_chains = 2, num_iter = 20, bayes_spec = set_ssvs(), sv_spec = set_sv(), include_mean = FALSE, minnesota = "longrun"))
#> Call:
#> bvhar_sv(y = etf_train, num_chains = 2, num_iter = 20, bayes_spec = set_ssvs(),
#> sv_spec = set_sv(), include_mean = FALSE, minnesota = "longrun")
#>
#> BVHAR with Stochastic Volatility
#> 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 282 variables
#> phi[1] phi[2] phi[3] phi[4] phi[5] phi[6] phi[7] phi[8]
#> 1 0.1923 -0.0150 0.0589 0.6196 0.1281 1.246 -0.20680 0.1381
#> 2 0.1916 -0.0904 0.0905 0.1604 0.0959 0.998 0.12531 -0.1326
#> 3 0.1250 0.1351 0.1232 -0.0715 0.2170 1.141 -0.11030 0.0732
#> 4 0.1413 0.0684 0.1015 0.1019 0.0335 1.021 -0.11200 0.0191
#> 5 0.0253 0.2031 0.2272 0.2522 0.1064 1.024 0.05655 0.0395
#> 6 0.0401 0.0835 0.1535 -0.0659 0.1374 0.987 0.10089 0.1364
#> 7 0.0833 0.1005 0.2023 0.0530 0.0677 0.877 -0.00198 0.0529
#> 8 0.1007 0.0625 0.0916 -0.0214 0.0285 0.875 0.07227 0.0386
#> 9 0.2086 0.0284 0.1603 -0.0781 0.1072 0.850 0.05214 0.0480
#> 10 0.1613 0.0654 0.1417 0.0144 0.0362 0.840 0.20474 0.0723
#> # ... with 10 more draws, and 274 more variables
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
(fit_hs_sv <- bvhar_sv(etf_train, num_chains = 2, num_iter = 20, bayes_spec = set_horseshoe(), sv_spec = set_sv(), include_mean = FALSE, minnesota = "longrun"))
#> Call:
#> bvhar_sv(y = etf_train, num_chains = 2, num_iter = 20, bayes_spec = set_horseshoe(),
#> sv_spec = set_sv(), include_mean = FALSE, minnesota = "longrun")
#>
#> BVHAR with Stochastic Volatility
#> 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 315 variables
#> phi[1] phi[2] phi[3] phi[4] phi[5] phi[6] phi[7] phi[8]
#> 1 1.81170 -0.04887 0.17674 0.1222 -0.96104 0.4752 -1.7864 1.6946
#> 2 0.42250 -0.05241 -0.09456 -0.0695 -0.40474 -0.2182 0.3485 1.2815
#> 3 0.00219 -0.12305 0.00252 -0.1339 0.09801 0.1174 0.5293 0.6035
#> 4 0.40343 -0.08217 -0.05841 0.3230 0.08070 0.0878 0.0715 0.5309
#> 5 0.48051 0.00716 -0.05710 0.1016 0.09021 0.1951 0.0800 -0.2272
#> 6 0.48797 0.03734 0.00408 0.0536 0.05661 0.6725 0.0156 0.4734
#> 7 0.45704 0.01631 0.00222 0.0204 -0.00182 1.1471 0.0322 0.1542
#> 8 0.57873 0.00778 0.03212 -0.2233 0.08151 1.1284 -0.0615 0.0838
#> 9 0.55219 0.00794 0.04163 -0.2964 0.12853 1.1669 0.0321 0.1459
#> 10 0.39159 0.06693 -0.00969 -0.0221 0.18191 1.0690 0.0112 -0.0573
#> # ... with 10 more draws, and 307 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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