add_data: Add data to an object of class 'gsmvar' defining a GMVAR, StMVAR, or G-StMVAR model

Description

add_data adds or updates data to object of class 'gsmvar' that defines a GMVAR, StMVAR, or G-StMVAR model. Also calculates mixing weights and quantile residuals accordingly.

Usage

add_data(data, gsmvar, calc_cond_moments = TRUE, calc_std_errors = FALSE)

Value

Returns an object of class 'gsmvar' defining the specified GSMVAR, StMVAR, or G-StMVAR model with the data added to the model. If the object already contained data, the data will be updated.

Arguments

data: a matrix or class 'ts' object with d>1 columns. Each column is taken to represent a univariate time series. NA values are not supported.
gsmvar: an object of class 'gsmvar', typically created with fitGSMVAR or GSMVAR.
calc_cond_moments: should conditional means and covariance matrices should be calculated? Default is TRUE if the model contains data and FALSE otherwise.
calc_std_errors: should approximate standard errors be calculated?

References

Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.
Virolainen S. 2025. A statistically identified structural vector autoregression with endogenously switching volatility regime. Journal of Business & Economic Statistics, 43, 1, 44-54.
Virolainen S. 2022. Gaussian and Student's t mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks in the Euro area. Unpublished working paper, available as arXiv:2109.13648.

Examples

Run this code

# GMVAR(1, 2), d=2 model:
params12 <- c(0.55, 0.112, 0.344, 0.055, -0.009, 0.718, 0.319, 0.005,
  0.03, 0.619, 0.173, 0.255, 0.017, -0.136, 0.858, 1.185, -0.012,
  0.136, 0.674)
mod12 <- GSMVAR(p=1, M=2, d=2, params=params12)
mod12

mod12_2 <- add_data(gdpdef, mod12)
mod12_2

# StMVAR(1, 2), d=2 model:
mod12t <- GSMVAR(p=1, M=2, d=2, params=c(params12, 10, 12), model="StMVAR")
mod12t
mod12t_2 <- add_data(gdpdef, mod12t)
mod12t_2

# Structural GMVAR(2, 2), d=2 model identified with sign-constraints:
params22s <- c(0.36, 0.121, 0.484, 0.072, 0.223, 0.059, -0.151, 0.395,
  0.406, -0.005, 0.083, 0.299, 0.218, 0.02, -0.119, 0.722, 0.093, 0.032,
  0.044, 0.191, 0.057, 0.172, -0.46, 0.016, 3.518, 5.154, 0.58)
W_22 <- matrix(c(1, 1, -1, 1), nrow=2, byrow=FALSE)
mod22s <- GSMVAR(p=2, M=2, d=2, params=params22s, structural_pars=list(W=W_22))
mod22s

mod22s_2 <- add_data(gdpdef, mod22s)
mod22s_2