gmvarkit (version 1.0.3)

diagnostic_plot: Quantile residual diagnostic plot for GMVAR model

Description

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

Usage

diagnostic_plot(gmvar, type = c("series", "ac", "ch", "norm"), maxlag = 10)

Arguments

gmvar

object of class 'gmvar', generated by function fitGMVAR() or GMVAR().

type

which type of diagnostic plot should be plotted?

• "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 kernel density estimates with theoretical standard normal density (dashed line) and standard normal QQ-plots.

maxlag

the maximum lag considered in types "ac" and "ch".

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 used. In the normality plot the kernel density estimates are calculated with function density() from the package stats with bandwith argument "SJ" and Gaussian kernel.

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 Unpublished Revision of HECER Discussion Paper No. 247.

See Also

fitGMVAR, GMVAR, quantile_residual_tests, 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)
diagnostic_plot(mod222c, type="ac", maxlag=12)
# }