gmvarkit (version 2.1.4)

cond_moment_plot: 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 'gsmvar', typically created with fitGSMVAR or GSMVAR.

which_moment

should conditional means or variances be plotted?

grid

add grid to the plots?

...

additional paramters passed to grid(...) plotting the grid if grid == TRUE.

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. 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 Unpublished working paper, available as arXiv:2109.13648.

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")