This function fits VHAR using OLS method.
vhar_lm(
y,
har = c(5, 22),
exogen = NULL,
s = 0,
include_mean = TRUE,
method = c("nor", "chol", "qr")
)# S3 method for vharlse
print(x, digits = max(3L, getOption("digits") - 3L), ...)
# S3 method for vharlse
logLik(object, ...)
# S3 method for vharlse
AIC(object, ...)
# S3 method for vharlse
BIC(object, ...)
is.vharlse(x)
# S3 method for vharlse
knit_print(x, ...)
vhar_lm() returns an object named vharlse
class. It is a list with the following components:
Coefficient Matrix
Fitted response values
Residuals
LS estimate for covariance matrix
Numer of Coefficients
Dimension of the data
Sample size used when training = totobs - month
Multivariate response matrix
3 (The number of terms. vharlse contains this element for usage in other functions.)
Order for weekly term
Order for monthly term
Total number of the observation
Process: VHAR
include constant term (const) or not (none)
VHAR linear transformation matrix
Design matrix of VAR(month)
Raw input
Solving method
Matched call
It is also a bvharmod class.
Time series data of which columns indicate the variables
Numeric vector for weekly and monthly order. By default, c(5, 22).
Exogenous variables
Lag of exogeneous variables in VHARX. By default, s = 0.
Add constant term (Default: TRUE) or not (FALSE)
Method to solve linear equation system.
(nor: normal equation (default), chol: Cholesky, and qr: HouseholderQR)
Any object
digit option to print
not used
A vharlse object
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.
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.
coef.vharlse(), residuals.vharlse(), and fitted.vharlse()
summary.vharlse() to summarize VHAR model
# 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))
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