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Summarizing Vector HAR Model

Source: R/summary-vharlse.R, R/print-vharlse.R
summary.vharlse.Rd

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.