Summarizing BVAR and BVHAR with Shrinkage Priors — print.summary.bvharsp • bvhar

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.