| quantile_residuals {gmvarkit} | R Documentation |
Calculate multivariate quantile residuals of a GMVAR, StMVAR, or G-StMVAR model
Description
quantile_residuals calculates multivariate quantile residuals
(proposed by Kalliovirta and Saikkonen 2010) for a GMVAR, StMVAR, or G-StMVAR model.
Usage
quantile_residuals(gsmvar)
Arguments
gsmvar |
an object of class |
Value
Returns ((n_obs-p) x d) matrix containing the multivariate quantile residuals,
j:th column corresponds to the time series in the j:th column of the data. The multivariate
quantile residuals are calculated so that the first column quantile residuals are the "unconditioned ones"
and the rest condition on all the previous ones in numerical order. Read the cited article by
Kalliovirta and Saikkonen 2010 for details.
References
Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.
Kalliovirta L. and Saikkonen P. 2010. Reliable Residuals for Multivariate Nonlinear Time Series Models. Unpublished Revision of HECER Discussion Paper No. 247.
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 Unpublished working paper, available as arXiv:2109.13648.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
fitGSMVAR, GSMVAR, quantile_residual_tests,
diagnostic_plot, predict.gsmvar, profile_logliks
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)
quantile_residuals(mod12)
# GMVAR(2,2), d=2 model with mean-parametrization:
params22 <- c(0.869, 0.549, 0.223, 0.059, -0.151, 0.395, 0.406, -0.005,
0.083, 0.299, 0.215, 0.002, 0.03, 0.576, 1.168, 0.218, 0.02, -0.119,
0.722, 0.093, 0.032, 0.044, 0.191, 1.101, -0.004, 0.105, 0.58)
mod22 <- GSMVAR(gdpdef, p=2, M=2, params=params22, parametrization="mean")
quantile_residuals(mod22)
# 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))
quantile_residuals(mod22s)