BVARSVResult#

class cultivars.multivariate.large_dim.volatility.BVARSVResult(*, endog, names, order, trend, prior_label, coefficients, deterministic, beta_mean, sigma_u, beta_draws, sigma_draws, h_draws, impact_draws, vol_of_vol, resid, fittedvalues, nobs, n_draws, n_burn, thin)[source]#

A fitted stochastic-volatility BVAR: one system, a path of covariances.

The family’s shared propagation surface, with the covariance read at the end of the sample: sigma_u and the covariance draws are Sigma_T = A^{-1} diag(exp h_T) A^{-T}, the covariance relevant for what comes next, and the predictive propagates each draw’s log-variance random walk forward from there. The full history lives in h_draws and volatility_path().

The triangular factorization is taken in the order of names at estimation time, so – as for the TVP-SV model – the recursive ordering is a structural declaration already made; orthogonalized responses inherit it.

Variables:

  • prior_label – Short description of the prior estimated under.

  • h_draws – (S, nobs, k) kept log-variance path draws.

  • impact_draws – (S, k, k) kept draws of the unit-lower A^{-1}.

  • vol_of_vol – (k,) posterior mean random-walk variances of the log volatilities.

  • n_draws – Total sampler iterations.

  • n_burn – Burn-in discarded.

  • thin – Post-burn thinning.

Attributes#

endog

names

order

trend

prior_label

coefficients

deterministic

beta_mean

sigma_u

beta_draws

sigma_draws

h_draws

impact_draws

vol_of_vol

resid

fittedvalues

nobs

n_draws

n_burn

thin

k_endog

Number of endogenous variables.

n_kept

Posterior draws retained.

observed

The effective sample the replications are shaped like, (nobs, k).

stable_share

Posterior probability that the system is stable.

Methods#

convergence(*chains[, include, exclude, ...])

Assess the sampler's convergence over the retained draws.

credible_interval(equation, regressor)

One coefficient's posterior summary: 16th percentile, mean, 84th.

forecast([steps, seed])

The posterior predictive: parameter and shock uncertainty.

forecast_paths([steps, seed])

The posterior predictive's raw simulated paths, one per kept draw.

irf([horizon, orthogonalized, cumulative, ...])

Posterior mean impulse responses.

irf_bands([horizon, orthogonalized, ...])

Posterior bands of the impulse responses.

posterior_replications([n_replications, seed])

Replicated effective samples, one per posterior draw used.

simulate([n, seed, burn, draw])

A fresh sample path from the fitted system at the posterior mean or at one draw.

summary()

The estimation summary, renderable as text, HTML, or a dataframe.

volatility_path(name)

One equation's posterior innovation standard deviation path.