Description Specifying the prior Estimating the model Processing the output

The mfbvar package makes estimation of Bayesian VARs with a mix of monthly and quarterly data
simple. The prior for the regression parameters is normal with Minnesota-style prior moments.
The package supports either an inverse Wishart prior for the error covariance matrix, yielding a
standard normal-inverse Wishart prior, or a time-varying error covariance matrix by means of a factor
stochastic volatility model through the `factorstochvol-package`

package.

The prior of the VAR model is specified using the function `set_prior`

. The function
creates a prior object, which can be further updated using `update_prior`

. The model can be
estimated using the steady-state prior, which requires the prior moments of the steady-state parameters.
The function `interval_to_moments`

is a helper function for obtaining these from prior intervals.

The model is estimated using the function `estimate_mfbvar`

. The error covariance matrix
is given an inverse Wishart prior or modeled using factor stochastic volatility. If the former is used,
`mdd`

can be used to estimate to the marginal data density (marginal likelihood).

Plots of the output can be obtained from calling the generic function `plot`

(see
`plot-mfbvar`

). If factor stochastic volatility is used, the time-varying
standard deviations can be plotted using `varplot`

. Predictions can be obtained
from `predict.mfbvar`

.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.