Finding the Set of Hyperparameters of Individual Bayesian Model

Instead of these functions, you can use choose_bayes().

Usage

choose_bvar(
  bayes_spec = set_bvar(),
  lower = 0.01,
  upper = 10,
  ...,
  eps = 1e-04,
  y,
  p,
  include_mean = TRUE,
  parallel = list()
)

choose_bvhar(
  bayes_spec = set_bvhar(),
  lower = 0.01,
  upper = 10,
  ...,
  eps = 1e-04,
  y,
  har = c(5, 22),
  include_mean = TRUE,
  parallel = list()
)

# S3 method for class 'bvharemp'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.bvharemp(x)

# S3 method for class 'bvharemp'
knit_print(x, ...)

Arguments

bayes_spec: Initial Bayes model specification.
lower: Lower bound. By default, .01.
upper: Upper bound. By default, 10.
...: not used
eps: Hyperparameter eps is fixed. By default, 1e-04.
y: Time series data
p: BVAR lag
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.
har: Numeric vector for weekly and monthly order. By default, c(5, 22).
x: bvharemp object
digits: digit option to print

Value

bvharemp class is a list that has

stats::optim() or optimParallel::optimParallel()
chosen bvharspec set
Bayesian model fit result with chosen specification
...

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

spec

Corresponding bvharspec

fit

Chosen Bayesian model

ml

Marginal likelihood of the final model

Details

Empirical Bayes method maximizes marginal likelihood and selects the set of hyperparameters. These functions implement L-BFGS-B method of stats::optim() to find the maximum of marginal likelihood.

If you want to set lower and upper option more carefully, deal with them like as in stats::optim() in order of set_bvar(), set_bvhar(), or set_weight_bvhar()'s argument (except eps). In other words, just arrange them in a vector.

References

Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM Journal on scientific computing, 16(5), 1190-1208.

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2013). Bayesian data analysis. Chapman and Hall/CRC.

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.