vhar_lm: Fitting Vector Heterogeneous Autoregressive Model

In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Fitting Vector Heterogeneous Autoregressive Model

Description

This function fits VHAR using OLS method.

Usage

vhar_lm(
  y,
  har = c(5, 22),
  include_mean = TRUE,
  method = c("nor", "chol", "qr")
)

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

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

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

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

is.vharlse(x)

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

Arguments

y	Time series data of which columns indicate the variables
har	Numeric vector for weekly and monthly order. By default, c(5, 22).
include_mean	Add constant term (Default: TRUE) or not (FALSE)
method	Method to solve linear equation system. (nor: normal equation (default), chol: Cholesky, and qr: HouseholderQR)
x	Any object
digits	digit option to print
...	not used
object	A vharlse object

Details

For VHAR model

Y_{t} = \Phi^{(d)} Y_{t - 1} + \Phi^{(w)} Y_{t - 1}^{(w)} + \Phi^{(m)} Y_{t - 1}^{(m)} + \epsilon_t

the function gives basic values.

Value

vhar_lm() returns an object named vharlse class. It is a list with the following components:

coefficients

Coefficient Matrix

fitted.values

Fitted response values

residuals

Residuals

covmat

LS estimate for covariance matrix

df

Numer of Coefficients

m

Dimension of the data

obs

Sample size used when training = totobs - month

y0

Multivariate response matrix

p

3 (The number of terms. vharlse contains this element for usage in other functions.)

week

Order for weekly term

month

Order for monthly term

totobs

Total number of the observation

process

Process: VHAR

type

include constant term (const) or not (none)

HARtrans

VHAR linear transformation matrix

design

Design matrix of VAR(month)

y

Raw input

method

Solving method

call

Matched call

It is also a bvharmod class.

References

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

Bubák, V., Kočenda, E., & Žikeš, F. (2011). Volatility transmission in emerging European foreign exchange markets. Journal of Banking & Finance, 35(11), 2829-2841.

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

See Also

• coef.vharlse(), residuals.vharlse(), and fitted.vharlse()

• summary.vharlse() to summarize VHAR model

Examples

# Perform the function using etf_vix dataset
fit <- vhar_lm(y = etf_vix)
class(fit)
str(fit)

# Extract coef, fitted values, and residuals
coef(fit)
head(residuals(fit))
head(fitted(fit))

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