VAR

class impulso.spec.VAR(*, lags, max_lags=None, prior='minnesota', volatility='constant')

Bases: ImpulsoBaseModel

Immutable VAR model specification.

Parameters:

• lags (int | Literal['aic', 'bic', 'hq'])

• max_lags (int | None)

• prior (Literal['minnesota'] | ~impulso.protocols.Prior)

• volatility (Literal['constant', 'sv'] | ~impulso.protocols.PyMCVolatilityProcess)

lags

Fixed lag order (int >= 1) or selection criterion string.

Type:

int | Literal[‘aic’, ‘bic’, ‘hq’]

max_lags

Upper bound for automatic selection. Only valid with string lags.

Type:

int | None

prior

Prior shorthand string or Prior protocol instance.

Type:

Literal[‘minnesota’] | impulso.protocols.Prior

volatility

Volatility shorthand string or PyMCVolatilityProcess protocol instance.

Type:

Literal[‘constant’, ‘sv’] | impulso.protocols.PyMCVolatilityProcess

Expand for references to impulso.spec.VAR

The Minnesota Prior / Usage in Impulso

Writing a Custom Prior / Using your custom prior

Choosing Lag Order / Fixed lag order

Impulso

The conjugate VAR: fast Bayesian estimation / When to reach for ConjugateVAR instead of the NUTS VAR / Scope

Probabilistic Forecasts

Identification in structural VARs: Cholesky vs sign restrictions / A case study using U.S. monetary policy data / Impulse Response Function

Oil supply news with an external instrument / How the announcement surprise identifies one shock

Fitting Your First Bayesian VAR

Stochastic volatility: modelling time-varying uncertainty / Model / Stochastic volatility inside a VAR

Structural Shocks in the Atmosphere

fit(data, sampler=None)

Estimate the Bayesian VAR model.

Parameters:

• data (VARData) – VARData instance.

• sampler (Sampler | None) – Sampler protocol instance. Defaults to NUTSSampler().

Returns:

FittedVAR with posterior draws.

Return type:

FittedVAR

Expand for references to impulso.spec.VAR.fit

Writing a Custom Prior / Using your custom prior

Impulso

Stochastic volatility: modelling time-varying uncertainty / Model / Stochastic volatility inside a VAR

model_config = {'arbitrary_types_allowed': True, 'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

property resolved_prior: Prior

Resolve string prior shorthand to a Prior instance.

property resolved_volatility: PyMCVolatilityProcess

Resolve string volatility shorthand to a PyMCVolatilityProcess instance.