bvarSvControlCreate#

Purpose#

Create a bvarSvControl structure with default values.

Format#

ctl = bvarSvControlCreate()#

Returns:

ctl (struct) –

An instance of a bvarSvControl structure with the following default values:

ctl.p

Scalar, lag order. Default = 1.

ctl.const

Scalar, 1 to include a constant, 0 to exclude. Default = 1.

ctl.b_prior

String, prior type for B coefficients.

`"minnesota"`	Minnesota prior with lambda hyperparameters. (Default)
`"flat"`	Diffuse prior with variance ctl.b_prior_var.
`"horseshoe"`	Horseshoe prior (Carvalho, Polson & Scott 2010) for adaptive shrinkage. Each coefficient gets a local shrinkage λ²_ij and a global τ² with half-Cauchy priors. Replaces Minnesota/SSVS for large systems (m=50+). Reuses ctl.b_prior_var as initial τ².

ctl.overall_tightness

Scalar, overall tightness (Minnesota only). Default = 0.2.

ctl.cross_shrinkage

Scalar, cross-variable shrinkage (Minnesota only). Default = 0.5.

ctl.lag_decay

Scalar, lag decay (Minnesota only). Default = 1.0.

ctl.constant_tightness

Scalar, constant tightness (Minnesota only). Default = 1e5.

ctl.exogenous_tightness

Scalar, exogenous tightness (Minnesota only). Default = 1.0.

ctl.soc_tightness

Scalar, sum-of-coefficients (Minnesota only). 0 = disabled. Default = 0.

ctl.sur_tightness

Scalar, single-unit-root (Minnesota only). 0 = disabled. Default = 0.

ctl.exogenous_scale

Scalar, exogenous regressor tightness (Minnesota only). Default = 1.0.

ctl.ar

Scalar, AR(1) prior mean for own lags (Minnesota only). 1.0 = random walk, 0.0 = white noise. Default = 1.0.

ctl.b_prior_var

Scalar, B prior variance (flat prior only). Default = 10.0.

ctl.sv_mu

Scalar, SV level prior mean. Default = 0.0.

ctl.sv_phi_mean

Scalar, SV persistence prior mean. Default = 0.97.

ctl.sv_phi_std

Scalar, SV persistence prior standard deviation. Default = 0.1.

ctl.sv_sigma2

Scalar, SV innovation variance scale. Default = 0.01.

ctl.ssvs

Scalar, enable SSVS variable selection. 0 = off (default), 1 = on.

ctl.ssvs_c0

Scalar, SSVS spike multiplier. Default = 0.1.

ctl.ssvs_c1

Scalar, SSVS slab multiplier. Default = 10.0.

ctl.ssvs_pi_b

Scalar, prior inclusion probability for B. Default = 0.5.

ctl.ssvs_pi_u

Scalar, prior inclusion probability for U off-diagonals. Default = 0.5.

ctl.ssvs_hierarchical

Scalar, 1 for hierarchical prior on inclusion probability, 0 for fixed. Default = 0.

ctl.n_draws

Scalar, number of posterior draws. Default = 5000.

ctl.n_burn

Scalar, burn-in draws. Default = 5000.

ctl.n_thin

Scalar, thinning interval. Default = 1.

ctl.seed

Scalar, RNG seed. Default = 42.

ctl.n_chains

Scalar, number of MCMC chains. Default = 1.

ctl.parallel

Scalar, 1 for parallel chains, 0 for sequential. Default = 0.

ctl.use_asis

Scalar, 1 to enable ASIS interweaving for SV (Kastner & Fruhwirth-Schnatter 2014). Default = 1.

ctl.sv_keep

String, storage mode for stochastic volatility draws.

`"full"`	Store all draws (default). Requires most memory.
`"last"`	Store only the last draw of log-volatilities per iteration.
`"online"`	Store running moments and a reservoir. Best for large systems.

ctl.reservoir_size

Scalar, reservoir size for sv_keep = "online". Default = 500.

ctl.u_bandwidth

Scalar, band-limited Cholesky U estimation. 0 = full lower-triangular U (default, m(m-1)/2 parameters). k > 0 = only first k off-diagonals per column estimated, rest fixed at zero. Reduces parameters from m(m-1)/2 to m*k. For m=50, k=3: 150 vs 1225 parameters. Note: this changes the model (approximation), not just computation.

ctl.quiet

Scalar, set to 1 to suppress printed output. Default = 0.

Examples#

new;
library timeseries;

ctl = bvarSvControlCreate();

// 4-chain SV-BVAR with SSVS
ctl.n_chains = 4;
ctl.parallel = 1;

fname = getGAUSSHome("pkgs/timeseries/examples/data/us_macro_quarterly.csv");
data = loadd(fname);
result = bvarSvFit(data, p=4, ssvs=1, n_draws=10000, n_burn=5000, ctl=ctl);

Library#

timeseries

Source#

bvar.src