| cond_moment_plot {gmvarkit} | R Documentation |
Conditional mean or variance plot for a GMVAR, StMVAR, or G-StMVAR model
Description
cond_moment_plot plots the one-step in-sample conditional means/variances of the model along with
the individual time series contained in the model (e.g. the time series the model was fitted to). Also plots
the regimewise conditional means/variances multiplied with mixing weights.
Usage
cond_moment_plot(
gsmvar,
which_moment = c("mean", "variance"),
grid = FALSE,
...
)
Arguments
gsmvar |
an object of class |
which_moment |
should conditional means or variances be plotted? |
grid |
add grid to the plots? |
... |
additional paramters passed to |
Details
The conditional mean plot works best if the data contains positive values only.
acf from the package stats and the plot method for class 'acf' objects is employed.
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 Unpublished working paper, available as arXiv:2109.13648.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
profile_logliks, fitGSMVAR, GSMVAR,
quantile_residual_tests, LR_test, Wald_test,
diagnostic_plot
Examples
# GMVAR(2, 2), d=2 model;
params22 <- c(0.36, 0.121, 0.223, 0.059, -0.151, 0.395, 0.406, -0.005,
0.083, 0.299, 0.215, 0.002, 0.03, 0.484, 0.072, 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)
cond_moment_plot(mod22, which_moment="mean")
cond_moment_plot(mod22, which_moment="variance")
cond_moment_plot(mod22, which_moment="mean", grid=TRUE, lty=3)
# G-StMVAR(2, 1, 1), d=2 model:
params22gs <- c(0.697, 0.154, 0.049, 0.374, 0.476, 0.318, -0.645, -0.302,
-0.222, 0.193, 0.042, -0.013, 0.048, 0.554, 0.033, 0.184, 0.005, -0.186,
0.683, 0.256, 0.031, 0.026, 0.204, 0.583, -0.002, 0.048, 0.182, 4.334)
mod22gs <- GSMVAR(gdpdef, p=2, M=c(1, 1), params=params22gs, model="G-StMVAR")
cond_moment_plot(mod22gs, which_moment="mean")
cond_moment_plot(mod22gs, which_moment="variance")
#StMVAR(4, 1), d=2 model:
params41t <- c(0.512, -0.002, 0.243, 0.024, -0.088, 0.452, 0.242, 0.011,
0.093, 0.162, -0.097, 0.033, -0.339, 0.19, 0.091, 0.006, 0.168, 0.101,
0.516, -0.005, 0.054, 4.417)
mod41t <- GSMVAR(gdpdef, p=4, M=1, params=params41t, model="StMVAR")
cond_moment_plot(mod41t, which_moment="mean")
cond_moment_plot(mod41t, which_moment="variance")