
add_data
adds or updates data to object of class 'gsmar
' that defines a GMAR, StMAR,
or G-StMAR model. Also calculates empirical mixing weights, conditional moments, and quantile residuals accordingly.
add_data(
data,
gsmar,
calc_qresiduals = TRUE,
calc_cond_moments = TRUE,
calc_std_errors = FALSE,
custom_h = NULL
)
a numeric vector or class 'ts'
object containing the data. NA
values are not supported.
object of class 'gsmar'
created with the function fitGSMAR
or GSMAR
.
should quantile residuals be calculated? Default is TRUE
iff the model contains data.
should conditional means and variances be calculated? Default is TRUE
iff the model contains data.
should approximate standard errors be calculated?
A numeric vector with same the length as the parameter vector: i:th element of custom_h is the difference
used in central difference approximation for partial differentials of the log-likelihood function for the i:th parameter.
If NULL
(default), then the difference used for differentiating overly large degrees of freedom parameters
is adjusted to avoid numerical problems, and the difference is 6e-6
for the other parameters.
Returns an object of class 'gsmar' defining the GMAR, StMAR, or G-StMAR model with the data added to the model. If the object already contained data, the data will be updated. Does not modify the 'gsmar' object given as argument!
Kalliovirta L., Meitz M. and Saikkonen P. 2015. Gaussian Mixture Autoregressive model for univariate time series. Journal of Time Series Analysis, 36, 247-266.
Meitz M., Preve D., Saikkonen P. 2018. A mixture autoregressive model based on Student's t-distribution. arXiv:1805.04010 [econ.EM].
Virolainen S. 2020. A mixture autoregressive model based on Gaussian and Student's t-distribution. arXiv:2003.05221 [econ.EM].
fitGSMAR
, GSMAR
, iterate_more
, get_gradient
,
get_regime_means
, swap_parametrization
, stmar_to_gstmar
# NOT RUN {
# GMAR model without data
params12 <- c(0.18, 0.93, 0.01, 0.86, 0.68, 0.02, 0.88)
gmar12 <- GSMAR(p=1, M=2, params=params12, model="GMAR")
gmar12
# Add data to the model
gmar12 <- add_data(data=logVIX, gmar12)
gmar12
# }
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