cond_moment_plot: Conditional mean or variance plot for GMAR, StMAR, and...

In uGMAR: Estimate Univariate Gaussian and Student's t Mixture Autoregressive Models

Conditional mean or variance plot for GMAR, StMAR, and G-StMAR models

Description

cond_moment_plot plots the one-step in-sample conditional means/variances of the model along with the 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 the mixing weights.

Usage

cond_moment_plot(gsmar, which_moment = c("mean", "variance"))

Arguments

gsmar	a class 'gsmar' object, typically generated by fitGSMAR or GSMAR.
which_moment	should conditional means or variances be plotted?

Details

The conditional mean plot works best if the data contains positive values only.

Value

cond_moment_plot only plots to a graphical device and does not return anything. Numerical values of the conditional means/variances can be extracted from the model with the dollar sign.

References

• Galbraith, R., Galbraith, J. 1974. On the inverses of some patterned matrices arising in the theory of stationary time series. Journal of Applied Probability 11, 63-71.

• Kalliovirta L. (2012) Misspecification tests based on quantile residuals. The Econometrics Journal, 15, 358-393.

• Kalliovirta L., Meitz M. and Saikkonen P. 2015. Gaussian Mixture Autoregressive model for univariate time series. Journal of Time Series Analysis, 36(2), 247-266.

• Meitz M., Preve D., Saikkonen P. 2023. A mixture autoregressive model based on Student's t-distribution. Communications in Statistics - Theory and Methods, 52(2), 499-515.

• Virolainen S. 2022. A mixture autoregressive model based on Gaussian and Student's t-distributions. Studies in Nonlinear Dynamics & Econometrics, 26(4) 559-580.

See Also

profile_logliks, diagnostic_plot, fitGSMAR, GSMAR, quantile_residual_tests, quantile_residual_plot

Examples

# GMAR model
params12 <- c(1.70, 0.85, 0.30, 4.12, 0.73, 1.98, 0.63)
gmar12 <- GSMAR(data=simudata, p=1, M=2, params=params12, model="GMAR")
cond_moment_plot(gmar12, which_moment="mean")
cond_moment_plot(gmar12, which_moment="variance")

# G-StMAR model
params42gs <- c(0.04, 1.34, -0.59, 0.54, -0.36, 0.01, 0.06, 1.28, -0.36,
                0.2, -0.15, 0.04, 0.19, 9.75)
gstmar42 <- GSMAR(data=M10Y1Y, p=4, M=c(1, 1), params=params42gs,
                  model="G-StMAR")
cond_moment_plot(gstmar42, which_moment="mean")
cond_moment_plot(gstmar42, which_moment="variance")

uGMAR documentation built on Aug. 19, 2023, 5:10 p.m.