set_lambda {bvhar}

Hyperpriors for Bayesian Models

Description

Set hyperpriors of Bayesian VAR and VHAR models.

Usage

set_lambda(mode = 0.2, sd = 0.4, lower = 1e-05, upper = 3)

set_psi(shape = 4e-04, scale = 4e-04, lower = 1e-05, upper = 3)

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

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

Arguments

mode

Mode of Gamma distribution. By default, .2.

sd

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

lower

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

upper

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

shape

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

scale

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

x

bvharpriorspec 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.

Value

bvharpriorspec object

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
)