| get_regime_autocovs {gmvarkit} | R Documentation |
Calculate regimewise autocovariance matrices
Description
get_regime_autocovs calculates the first p regimewise autocovariance
matrices \Gamma_{m}(j) for the given GMVAR, StMVAR, or G-StMVAR model.
Usage
get_regime_autocovs(gsmvar)
Arguments
gsmvar |
an object of class |
Value
Returns an (d x d x p+1 x M) array containing the first p regimewise autocovariance matrices.
The subset [, , j, m] contains the j-1:th lag autocovariance matrix of the m:th regime.
References
Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.
Lütkepohl H. 2005. New Introduction to Multiple Time Series Analysis, Springer.
McElroy T. 2017. Computation of vector ARMA autocovariances. Statistics and Probability Letters, 124, 92-96.
Virolainen S. (forthcoming). A statistically identified structural vector autoregression with endogenously switching volatility regime. Journal of Business & Economic Statistics.
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 Unpublished working paper, available as arXiv:2109.13648.
See Also
Other moment functions:
cond_moments(),
get_regime_means(),
uncond_moments()
Examples
# 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(gdpdef, p=1, M=2, params=params12)
get_regime_autocovs(mod12)
# 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(gdpdef, p=2, M=2, params=params22s, structural_pars=list(W=W_22))
mod22s
get_regime_autocovs(mod22s)