gmvarkit (version 1.4.1)

diagnostic_plot: Quantile residual diagnostic plot for a GMVAR 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(
  gmvar,
  type = c("all", "series", "ac", "ch", "norm"),
  maxlag = 12,
  wait_time = 4
)

Arguments

gmvar

an object of class 'gmvar' created with fitGMVAR or GMVAR.

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 Unpublished Revision of HECER Discussion Paper No. 247.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. 2020. Structural Gaussian mixture vector autoregressive model. Unpublished working paper, available as arXiv:2007.04713.

See Also

profile_logliks, fitGMVAR, GMVAR, quantile_residual_tests, LR_test, Wald_test, cond_moment_plot, acf, density, predict.gmvar

Examples

# NOT RUN {
# These examples use the data 'eurusd' which comes with the
# package, but in a scaled form.
data <- cbind(10*eurusd[,1], 100*eurusd[,2])
colnames(data) <- colnames(eurusd)

# GMVAR(1,2), d=2 model:
params122 <- c(0.623, -0.129, 0.959, 0.089, -0.006, 1.006, 1.746,
 0.804, 5.804, 3.245, 7.913, 0.952, -0.037, -0.019, 0.943, 6.926,
 3.982, 12.135, 0.789)
mod122 <- GMVAR(data, p=1, M=2, params=params122)
diagnostic_plot(mod122, type="series")
diagnostic_plot(mod122, type="ac")

# GMVAR(2,2), d=2 model:
params222 <-  c(1.386, -0.765, 1.314, 0.145, 0.094, 1.292, -0.389,
 -0.070, -0.109, -0.281, 0.920, -0.025, 4.839, 1.005, 5.928, 1.248,
  0.077, -0.040, 1.266, -0.272, -0.074, 0.034, -0.313, 5.855, 3.570,
  9.838, 0.740)
mod222 <- GMVAR(data, p=2, M=2, params=params222)
diagnostic_plot(mod222, type="ch")
diagnostic_plot(mod222, type="norm")

# GMVAR(2,2), d=2 model with AR-parameters restricted to be
# the same for both regimes:
C_mat <- rbind(diag(2*2^2), diag(2*2^2))
params222c <- c(1.031, 2.356, 1.786, 3.000, 1.250, 0.060, 0.036,
 1.335, -0.290, -0.083, -0.047, -0.356, 0.934, -0.152, 5.201, 5.883,
 3.560, 9.799, 0.368)
mod222c <- GMVAR(data, p=2, M=2, params=params222c, constraints=C_mat)
diagnostic_plot(mod222c, wait_time=0.1)
diagnostic_plot(mod222c, type="ac", maxlag=12)
# }