choose_bvar {bvhar} | R Documentation |
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), ...)
is.bvharemp(x)
## S3 method for class 'bvharemp'
knit_print(x, ...)
Arguments
bayes_spec |
Initial Bayes model specification. |
lower |
|
upper |
|
... |
not used |
eps |
Hyperparameter |
y |
Time series data |
p |
BVAR lag |
include_mean |
Add constant term (Default: |
parallel |
List the same argument of |
har |
Numeric vector for weekly and monthly order. By default, |
x |
Any 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
chosen
bvharspec
setBayesian model fit result with chosen specification
- ...
Many components of
stats::optim()
oroptimParallel::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. (2024). Bayesian vector heterogeneous autoregressive modeling. Journal of Statistical Computation and Simulation, 94(6), 1139-1157.