bvhar (version 2.2.2)

set_lambda: Hyperpriors for Bayesian Models

Description

Set hyperpriors of Bayesian VAR and VHAR models.

Usage

set_lambda(
  mode = 0.2,
  sd = 0.4,
  param = NULL,
  lower = 1e-05,
  upper = 3,
  grid_size = 100L
)
set_psi(shape = 4e-04, scale = 4e-04, lower = 1e-05, upper = 3)
# S3 method for bvharpriorspec
print(x, digits = max(3L, getOption("digits") - 3L), ...)
is.bvharpriorspec(x)
# S3 method for bvharpriorspec
knit_print(x, ...)

Value

bvharpriorspec object

Arguments

mode

Mode of Gamma distribution. By default, .2.

sd

Standard deviation of Gamma distribution. By default, .4.

param

Shape and rate of Gamma distribution, in the form of c(shape, rate). If specified, ignore mode and sd.

lower

Lower bound for stats::optim(). By default, 1e-5.

upper

Upper bound for stats::optim(). By default, 3.

grid_size

Griddy gibbs grid size for lag scaling

shape

Shape of Inverse Gamma distribution. By default, (.02)^2.

scale

Scale of Inverse Gamma distribution. By default, (.02)^2.

x

Any object

digits

digit option to print

...

not used

Details

In addition to Normal-IW priors set_bvar(), set_bvhar(), and set_weight_bvhar(), these functions give hierarchical structure to the model.

• set_lambda() specifies hyperprior for \(\lambda\) (lambda), which is Gamma distribution.

• set_psi() specifies hyperprior for \(\psi / (\nu_0 - k - 1) = \sigma^2\) (sigma), which is Inverse gamma distribution.

The following set of (mode, sd) are recommended by Sims and Zha (1998) for set_lambda().

• (mode = .2, sd = .4): default

• (mode = 1, sd = 1)

Giannone et al. (2015) suggested data-based selection for set_psi(). It chooses (0.02)^2 based on its empirical data set.

References

Giannone, D., Lenza, M., & Primiceri, G. E. (2015). Prior Selection for Vector Autoregressions. Review of Economics and Statistics, 97(2).

Examples

# Hirearchical BVAR specification------------------------
set_bvar(
  sigma = set_psi(shape = 4e-4, scale = 4e-4),
  lambda = set_lambda(mode = .2, sd = .4),
  delta = rep(1, 3),
  eps = 1e-04 # eps = 1e-04
)