mfbvar (version 0.5.6)

estimate_mfbvar: Mixed-frequency Bayesian VAR

Description

The main function for estimating a mixed-frequency BVAR.

Usage

estimate_mfbvar(mfbvar_prior = NULL, prior, variance = "iw", ...)

Arguments

mfbvar_prior

a mfbvar_prior object

prior

either "ss" (steady-state prior), "ssng" (hierarchical steady-state prior with normal-gamma shrinkage) or "minn" (Minnesota prior)

variance

form of the error variance-covariance matrix: "iw" for the inverse Wishart prior, "diffuse" for a diffuse prior, "csv" for common stochastic volatility or "fsv" for factor stochastic volatility

...

additional arguments to update_prior (if mfbvar_prior is NULL, the arguments are passed on to set_prior)

Value

An object of class mfbvar, mfbvar_<prior> and mfbvar_<prior>_<variance> containing posterior quantities as well as the prior object. For all choices of prior and variance, the returned object contains:

Pi

Array of dynamic coefficient matrices; Pi[,, r] is the rth draw

Z

Array of monthly processes; Z[,, r] is the rth draw

Z_fcst

Array of monthly forecasts; Z_fcst[,, r] is the rth forecast. The first n_lags rows are taken from the data to offer a bridge between observations and forecasts and for computing nowcasts (i.e. with ragged edges).

Steady-state priors If prior = "ss", it also includes:

roots

The maximum eigenvalue of the lag polynomial (if check_roots = TRUE)

If prior = "ssng", it also includes:

psi

Matrix of steady-state parameter vectors; psi[r,] is the rth draw

roots

The maximum eigenvalue of the lag polynomial (if check_roots = TRUE)

lambda_psi

Vector of draws of the global hyperparameter in the normal-Gamma prior

phi_psi

Vector of draws of the auxiliary hyperparameter in the normal-Gamma prior

omega_psi

Matrix of draws of the prior variances of psi; omega_psi[r, ] is the rth draw, where diag(omega_psi[r, ]) is used as the prior covariance matrix for psi

Constant error covariances If variance = "iw" or variance = "diffuse", it also includes:

Time-varying error covariances If variance = "csv", it also includes:

phi

Vector of AR(1) parameters for the log-volatility regression; phi[r] is the rth draw

sigma

Vector of error standard deviations for the log-volatility regression; sigma[r] is the rth draw

f

Matrix of log-volatilities; f[r, ] is the rth draw

If variance = "fsv", it also includes:

latent

Array of latent log-volatilities; latent[,, r] is the rth draw

mu

Matrix of means of the log-volatilities; mu[, r] is the rth draw

phi

Matrix of AR(1) parameters for the log-volatilities; phi[, r] is the rth draw

sigma

Matrix of innovation variances for the log-volatilities; sigma[, r] is the rth draw

References

Ankargren, S., Unosson, M., & Yang, Y. (2020) A Flexible Mixed-Frequency Bayesian Vector Autoregression with a Steady-State Prior. Journal of Time Series Econometrics, 12(2), 10.1515/jtse-2018-0034. Ankargren, S., & Jon<U+00E9>us, P. (2020) Simulation Smoothing for Nowcasting with Large Mixed-Frequency VARs. Econometrics and Statistics, 10.1016/j.ecosta.2020.05.007. Ankargren, S., & Jon<U+00E9>us, P. (2019) Estimating Large Mixed-Frequency Bayesian VAR Models. arXiv:1912.02231, https://arxiv.org/abs/1912.02231. Kastner, G., & Huber, F. (2020) Sparse Bayesian Vector Autoregressions in Huge Dimensions. Journal of Forecasting, 39, 1142--1165. 10.1002/for.2680. Schorfheide, F., & Song, D. (2015) Real-Time Forecasting With a Mixed-Frequency VAR. Journal of Business & Economic Statistics, 33(3), 366--380. 10.1080/07350015.2014.954707

See Also

set_prior, update_prior, predict.mfbvar, plot.mfbvar_minn, plot.mfbvar_ss, varplot, summary.mfbvar

Examples

# NOT RUN {
prior_obj <- set_prior(Y = mf_usa, n_lags = 4, n_reps = 20)
mod_minn <- estimate_mfbvar(prior_obj, prior = "minn")
# }