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