summary.mvgam_irf

This function takes an <code>mvgam_irf</code> object and
calculates a posterior summary of the impulse responses of each
series to shocks from each of the other series, at all horizons

Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.

Nicholas J Clark

mvgam

Multivariate (Dynamic) Generalized Additive Models

summary.mvgam_irf function

<dl><dt>object</dt>
<dd>an object of class <code>mvgam_irf</code> obtained using the
<code>irf()</code> function. This object will contain draws from the posterior
distribution of the impulse responses.</dd>
<dt>probs</dt>
<dd>The upper and lower percentiles to be computed by the <code>quantile</code> function,
in addition to the median</dd>
<dt>...</dt>
<dd>ignored</dd></dl>

Arguments

Author

Posterior summary of impulse responses — summary.mvgam_irf

<dl>

<dt>object</dt>
<dd>an object of class <code>mvgam_irf</code> obtained using the
<code>irf()</code> function. This object will contain draws from the posterior
distribution of the impulse responses.</dd>


<dt>probs</dt>
<dd>The upper and lower percentiles to be computed by the <code>quantile</code> function,
in addition to the median</dd>


<dt>...</dt>
<dd>ignored</dd>

</dl>

summary.mvgam_irf: Posterior summary of impulse responses

Description

Usage

Value

Arguments

Author

See Also