SVPosterior#

cultivars.univariate.stochastic_volatility.SVPosterior[source]#: alias of _SVPosterior

Attributes#

`endog`
`mean_spec`
`method`
`mu_draws`
`phi_draws`
`sigma2_draws`
`mean_draws`
`h_draws`
`nu_draws`
`rho_draws`
`loglik_draws`
`acceptance_rate`
`nobs`
`n_draws`
`n_burn`
`thin`
`log_variance`	Posterior-mean log-variance path, `(n,)`.
`n_kept`	Posterior draws retained.
`observed`	The series the replications are shaped like, `(n,)`.
`params`	The posterior-mean parameter record.
`state_space`	The model at the posterior mean, on the nonlinear substrate.
`volatility`	Posterior-mean volatility `E[exp(h_t / 2) \| y]`, `(n,)`.
`fittedvalues`
`resid`

`convergence`(*chains[, include, exclude, ...])	Assess the sampler's convergence over the retained draws.
`posterior_replications`([n_replications, seed])	Replicated series, one per posterior draw used.
`simulate`([n, seed, burn, draw])	A fresh sample path at the posterior mean or at one kept draw.
`summary`()	The estimation summary, renderable as text, HTML, or a dataframe.
`to_dict`()	The aligned per-observation outputs as plain numpy arrays.
`to_pandas`(*[, index])	The aligned per-observation outputs as a `pandas.DataFrame`.
`to_polars`()	The aligned per-observation outputs as a `polars.DataFrame`.
`volatility_quantiles`([levels])	Pointwise posterior quantiles of the volatility, `(len(levels), n)`.