diagnostic_plot: Quantile residual diagnostic plot for a GMVAR, StMVAR, or...

In saviviro/gmvarkit: Estimate Gaussian and Student's t Mixture Vector Autoregressive Models

Quantile residual diagnostic plot for a GMVAR, StMVAR, or G-StMVAR model

Description

diagnostic_plot plots a multivariate quantile residual diagnostic plot for either autocorrelation, conditional heteroskedasticity, or normality, or simply draws the quantile residual time series.

Usage

diagnostic_plot(
  gsmvar,
  type = c("all", "series", "ac", "ch", "norm"),
  maxlag = 12,
  wait_time = 4
)

Arguments

gsmvar	an object of class 'gsmvar', typically created with fitGSMVAR or GSMVAR.
type	which type of diagnostic plot should be plotted? "all" all below sequentially. "series" the quantile residual time series. "ac" the quantile residual autocorrelation and cross-correlation functions. "ch" the squared quantile residual autocorrelation and cross-correlation functions. "norm" the quantile residual histogram with theoretical standard normal density (dashed line) and standard normal QQ-plots.
maxlag	the maximum lag considered in types "ac" and "ch".
wait_time	if type == all how many seconds to wait before showing next figure?

Details

Auto- and cross-correlations (types "ac" and "ch") are calculated with the function 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.

• 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 Unpublished Revision of HECER Discussion Paper No. 247.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. 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 Unpublished working paper, available as arXiv:2109.13648.

• Virolainen S. 2025. A statistically identified structural vector autoregression with endogenously switching volatility regime. Journal of Business & Economic Statistics, 43, 1, 44-54.

See Also

profile_logliks, fitGSMVAR, GSMVAR, quantile_residual_tests, LR_test, Wald_test, Rao_test, cond_moment_plot, acf, density, predict.gsmvar

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)
diagnostic_plot(mod12, type="series")
diagnostic_plot(mod12, type="ac")

# 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)
diagnostic_plot(mod22, type="ch")
diagnostic_plot(mod22, type="norm")

# 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")
diagnostic_plot(mod22gs, wait_time=0)

saviviro/gmvarkit documentation built on Jan. 4, 2025, 12:52 p.m.