uGMAR (version 3.4.0)

add_data: Add data to object of class 'gsmar' defining a GMAR, StMAR, or G-StMAR model

Description

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.

Usage

add_data(
  data,
  gsmar,
  calc_qresiduals = TRUE,
  calc_cond_moments = TRUE,
  calc_std_errors = FALSE,
  custom_h = NULL
)

Arguments

data

a numeric vector or class 'ts' object containing the data. NA values are not supported.

gsmar

a class 'gsmar' object, typically generated by fitGSMAR or GSMAR.

calc_qresiduals

should quantile residuals be calculated? Default is TRUE iff the model contains data.

calc_cond_moments

should conditional means and variances be calculated? Default is TRUE iff the model contains data.

calc_std_errors

should approximate standard errors be calculated?

custom_h

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.

Value

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!

References

• 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. 2021. A mixture autoregressive model based on Student's t-distribution. Communications in Statistics - Theory and Methods, doi: 10.1080/03610926.2021.1916531

• Virolainen S. 2021. A mixture autoregressive model based on Gaussian and Student's t-distributions. Studies in Nonlinear Dynamics & Econometrics, doi: 10.1515/snde-2020-0060

See Also

fitGSMAR, GSMAR, iterate_more, get_gradient, get_regime_means, swap_parametrization, stmar_to_gstmar

Examples

# NOT RUN {
# G-StMAR model without data
params42gs <- c(0.04, 1.34, -0.59, 0.54, -0.36, 0.01, 0.06, 1.28, -0.36,
                0.2, -0.15, 0.04, 0.19, 9.75)
gstmar42 <- GSMAR(p=4, M=c(1, 1), params=params42gs, model="G-StMAR")
gstmar42

# Add data to the model
gstmar42 <- add_data(data=M10Y1Y, gsmar=gstmar42)
gstmar42
# }