choose_bvar {bvhar}

Finding the Set of Hyperparameters of Individual Bayesian Model

Description

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), ...)

## 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

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.

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

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. (2023+). Bayesian vector heterogeneous autoregressive modeling. Journal of Statistical Computation and Simulation.