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bvhar (version 2.2.2)

choose_bayes: Finding the Set of Hyperparameters of Bayesian Model

Description

[Experimental] 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()
)

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

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.

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()