choose_bayes: Finding the Set of Hyperparameters of Bayesian Model

In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Finding the Set of Hyperparameters of Bayesian Model

Description

This function chooses the set of hyperparameters of Bayesian model using stats::optim() function.

Usage

choose_bayes(
  bayes_bound = bound_bvhar(),
  ...,
  eps = 1e-04,
  y,
  order = c(5, 22),
  include_mean = TRUE,
  parallel = list()
)

Arguments

bayes_bound	Empirical Bayes optimization bound specification defined by bound_bvhar().
...	Additional arguments for stats::optim().
eps	Hyperparameter eps is fixed. By default, 1e-04.
y	Time series data
order	Order for BVAR or BVHAR. p of bvar_minnesota() or har of bvhar_minnesota(). By default, c(5, 22) for har.
include_mean	Add constant term (Default: TRUE) or not (FALSE)
parallel	List the same argument of optimParallel::optimParallel(). By default, this is empty, and the function does not execute parallel computation.

Value

bvharemp class is a list that has

...

Many components of stats::optim() or optimParallel::optimParallel()

spec

Corresponding bvharspec

fit

Chosen Bayesian model

ml

Marginal likelihood of the final model

References

Giannone, D., Lenza, M., & Primiceri, G. E. (2015). Prior Selection for Vector Autoregressions. Review of Economics and Statistics, 97(2).

Kim, Y. G., and Baek, C. (2024). Bayesian vector heterogeneous autoregressive modeling. Journal of Statistical Computation and Simulation, 94(6), 1139-1157.

See Also

• bound_bvhar() to define L-BFGS-B optimization bounds.

• Individual functions: choose_bvar()

bvhar documentation built on April 4, 2025, 5:22 a.m.