summary.bvharsp: Summarizing BVAR and BVHAR with Shrinkage Priors

In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Summarizing BVAR and BVHAR with Shrinkage Priors

Description

Conduct variable selection.

Usage

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

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

## S3 method for class 'ssvsmod'
summary(object, method = c("pip", "ci"), threshold = 0.5, level = 0.05, ...)

## S3 method for class 'hsmod'
summary(object, method = c("pip", "ci"), threshold = 0.5, level = 0.05, ...)

## S3 method for class 'ngmod'
summary(object, level = 0.05, ...)

Arguments

x	summary.bvharsp object
digits	digit option to print
...	not used
object	Model fit
method	Use PIP (pip) or credible interval (ci).
threshold	Threshold for posterior inclusion probability
level	Specify alpha of credible interval level 100(1 - alpha) percentage. By default, .05.

Value

summary.ssvsmod object

hsmod object

ngmod object

References

George, E. I., & McCulloch, R. E. (1993). Variable Selection via Gibbs Sampling. Journal of the American Statistical Association, 88(423), 881-889.

George, E. I., Sun, D., & Ni, S. (2008). Bayesian stochastic search for VAR model restrictions. Journal of Econometrics, 142(1), 553-580.

Koop, G., & Korobilis, D. (2009). Bayesian Multivariate Time Series Methods for Empirical Macroeconomics. Foundations and Trends® in Econometrics, 3(4), 267-358.

O’Hara, R. B., & Sillanpää, M. J. (2009). A review of Bayesian variable selection methods: what, how and which. Bayesian Analysis, 4(1), 85-117.

bvhar documentation built on April 4, 2025, 5:22 a.m.