summary.vharlse {bvhar}

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

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.