mdd.mfbvar_ss_iw

object of class <code>mfbvar_ss</code>

option for which method to choose for computing the mdd (<code>1</code> or <code>2</code>)

method

additional arguments (currently only <code>p_trunc</code> for the degree of truncation for method 2 is available)

Estimate the marginal data density for the model with a steady-state prior.

Functions and tools for estimation of mixed-frequency Bayesian vector autoregressive (VAR) models. The package implements a state space-based VAR model that handles mixed frequencies of the data as proposed by Schorfheide and Song (2015) <doi:10.1080/07350015.2014.954707>, and extensions thereof developed by Ankargren, Unosson and Yang (2020) <doi:10.1515/jtse-2018-0034>, Ankargren and Joneus (2019) <arXiv:1912.02231>, and Ankargren and Joneus (2020) <doi:10.1016/j.ecosta.2020.05.007>. The models are estimated using Markov Chain Monte Carlo to numerically approximate the posterior distribution. Prior distributions that can be used include normal-inverse Wishart and normal-diffuse priors as well as steady-state priors. Stochastic volatility can be handled by common or factor stochastic volatility models.

Sebastian Ankargren

mdd.mfbvar_ss_iw: Marginal data density method for class `mfbvar_ss`

Description

Usage

Arguments

Value

Details

References

See Also