StochasticVolatilitySVAR.identify#

StochasticVolatilitySVAR.identify(*, n_draws=3000, n_burn=1000, thin=1, prior=None, seed=None)[source]#

Sample the posterior over impact matrices and volatility paths.

The prior on each shock’s volatility law is the Kim-Shephard-Chib one: Beta (a, b) on (phi + 1) / 2 and inverse-gamma (shape, rate) on the innovation variance. The impact matrix carries a weak Gaussian prior on the rows of B**-1 scaled from the reduced-form covariance.

Parameters:

  • n_draws (int) – Total sampler iterations.

  • n_burn (int) – Burn-in discarded.

  • thin (int) – Keep every thin-th post-burn draw.

  • prior (_VolatilityPrior | None) – The prior on each shock’s volatility law, an instance of VolatilityPrior or None to use the default Kim-Shephard-Chib prior.

  • seed (int | Generator | None) – Seed or generator.

Return type:

StochasticVolatilitySVARResult

Returns:

The StochasticVolatilitySVARResult.

Raises: