choose_bayes {bvhar} | R Documentation |
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 |
... |
Additional arguments for |
eps |
Hyperparameter |
y |
Time series data |
order |
Order for BVAR or BVHAR. |
include_mean |
Add constant term (Default: |
parallel |
List the same argument of |
Value
bvharemp
class is a list that has
- ...
Many components of
stats::optim()
oroptimParallel::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()