View source: R/loglikelihood.R
Pearson_residuals | R Documentation |
Pearson_residuals
calculates multivariate Pearson residuals for a GMVAR, StMVAR, or G-StMVAR model.
Pearson_residuals(gsmvar, standardize = TRUE)
gsmvar |
an object of class |
standardize |
Should the residuals be standardized? Use |
Returns ((n_obs-p) x d)
matrix containing the residuals,
j
:th column corresponds to the time series in the j
:th column of the data.
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 Unpublished Revision of HECER Discussion Paper No. 247.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. 2022. Structural Gaussian mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks. Unpublished working paper, available as arXiv:2007.04713.
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 Unpublished working paper, available as arXiv:2109.13648.
fitGSMVAR
, GSMVAR
, quantile_residuals
,
diagnostic_plot
# 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)
Pearson_residuals(mod12, standardize=FALSE) # Raw residuals
Pearson_residuals(mod12, standardize=TRUE) # Standardized to identity cov.matrix.
# 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))
Pearson_residuals(mod22s, standardize=FALSE) # Raw residuals
Pearson_residuals(mod22s, standardize=TRUE) # Standardized to identity cov.matrix.
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