bvarControlCreate#

Purpose#

Create a bvarControl structure with default values.

Format#

ctl = bvarControlCreate()#

Returns:

ctl (struct) –

An instance of a bvarControl 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.prior

String, prior type.

`"minnesota"`	Conjugate Normal-Inverse-Wishart Minnesota prior. (Default)
`"flat"`	Diffuse prior with Gibbs sampling.

ctl.overall_tightness

Scalar, overall tightness. Controls how much data vs prior matters. Smaller values = tighter prior. Default = 0.2.

ctl.cross_shrinkage

Scalar, cross-variable shrinkage. Other variables’ lags are shrunk by this factor relative to own lags. Default = 0.5.

ctl.lag_decay

Scalar, lag decay. Higher lags are shrunk by \(\ell^{-\lambda_3}\). Default = 1.0.

ctl.constant_tightness

Scalar, constant tightness. Default = 1e5 (effectively uninformative).

ctl.exogenous_tightness

Scalar, exogenous variable tightness. Default = 1.0.

ctl.soc_tightness

Scalar, sum-of-coefficients tightness (Doan, Litterman & Sims 1984). Set to 0 to disable. Typical range: 1-10. Default = 0 (disabled).

ctl.sur_tightness

Scalar, single-unit-root tightness (Sims 1993). Set to 0 to disable. Typical range: 1-10. Default = 0 (disabled).

ctl.exogenous_scale

Scalar, exogenous regressor prior tightness. Default = 1.0.

ctl.ar

Scalar, AR(1) prior mean for own-lag coefficients.

1.0	Random walk prior (for levels data). (Default)
0.0	White noise prior (for stationary/growth rate data).

ctl.alpha0

Scalar, Inverse-Wishart degrees of freedom. Default = 0, which uses m+2 (least informative proper prior).

ctl.n_draws

Scalar, number of posterior draws. Default = 5000.

ctl.seed

Scalar, random number generator seed for reproducibility. Default = 42.

ctl.quiet

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

Examples#

new;
library timeseries;

fname = getGAUSSHome("pkgs/timeseries/examples/data/us_macro_quarterly.csv");
data = loadd(fname);

// Minnesota BVAR(4) with tighter prior
result = bvarFit(data, p=4, overall_tightness=0.1, n_draws=10000);

Library#

timeseries

Source#

bvar.src