R: Finding the Set of Hyperparameters of Bayesian Model

choose_bayes {bvhar}

R Documentation

Finding the Set of Hyperparameters of Bayesian Model

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

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

`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.

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

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. (n.d.). Bayesian vector heterogeneous autoregressive modeling. submitted.