summary.vharlse: Summarizing Vector HAR Model

In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Summarizing Vector HAR Model

Description

summary method for vharlse class.

Usage

## S3 method for class 'vharlse'
summary(object, ...)

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

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

Arguments

object	A vharlse object
...	not used
x	summary.vharlse object
digits	digit option to print
signif_code	Check significant rows (Default: TRUE)

Value

summary.vharlse class additionally computes the following

names	Variable names
totobs	Total number of the observation
obs	Sample size used when training = totobs - p
p	3
week	Order for weekly term
month	Order for monthly term
coefficients	Coefficient Matrix
call	Matched call
process	Process: VAR
covmat	Covariance matrix of the residuals
corrmat	Correlation matrix of the residuals
roots	Roots of characteristic polynomials
is_stable	Whether the process is stable or not based on roots
log_lik	log-likelihood
ic	Information criteria vector

• AIC - AIC

• BIC - BIC

• HQ - HQ

• FPE - FPE

References

Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.

Corsi, F. (2008). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.

Baek, C. and Park, M. (2021). Sparse vector heterogeneous autoregressive modeling for realized volatility. J. Korean Stat. Soc. 50, 495-510.

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