set_horseshoe {bvhar}

Horseshoe Prior Specification

Description

Set initial hyperparameters and parameter before starting Gibbs sampler for Horseshoe prior.

Usage

set_horseshoe(local_sparsity = 1, global_sparsity = 1)

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

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

Arguments

local_sparsity

Initial local shrinkage hyperparameters

global_sparsity

Initial global shrinkage hyperparameter

x

horseshoespec

digits

digit option to print

...

not used

Details

Set horseshoe prior initialization for VAR family.

• local_sparsity: Local shrinkage for each row of coefficients matrix.

• global_sparsity: (Initial) global shrinkage.

• init_cov: Initial covariance matrix.

In this package, horseshoe prior model is estimated by Gibbs sampling, initial means initial values for that gibbs sampler.

References

Carvalho, C. M., Polson, N. G., & Scott, J. G. (2010). The horseshoe estimator for sparse signals. Biometrika, 97(2), 465–480.

Makalic, E., & Schmidt, D. F. (2016). A Simple Sampler for the Horseshoe Estimator. IEEE Signal Processing Letters, 23(1), 179–182.