uGMAR (version 3.2.4)

quantileResidualPlot: Plot quantile residual time series and histogram

Description

quantileResidualsPlot plots quantile residual time series and histogram.

Usage

quantileResidualPlot(gsmar)

Arguments

gsmar

object of class 'gsmar' created with the function fitGSMAR or GSMAR.

Value

Only plots to a graphical device and doesn't return anything.

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, 247-266.

• Meitz M., Preve D., Saikkonen P. 2018. A mixture autoregressive model based on Student's t-distribution. arXiv:1805.04010 [econ.EM].

• Virolainen S. 2020. A mixture autoregressive model based on Gaussian and Student's t-distribution. arXiv:2003.05221 [econ.EM].

See Also

profile_logliks, diagnosticPlot, fitGSMAR, GSMAR, quantileResidualTests, simulateGSMAR

Examples

# NOT RUN {
# GMAR model
fit12 <- fitGSMAR(data=logVIX, p=1, M=2, model="GMAR")
quantileResidualPlot(fit12)

# Non-mixture version of StMAR model
fit11t <- fitGSMAR(logVIX, 1, 1, model="StMAR", ncores=1, ncalls=1)
quantileResidualPlot(fit11t)

# Restricted G-StMAR-model
fit12gsr <- fitGSMAR(logVIX, 1, M=c(1, 1), model="G-StMAR",
 restricted=TRUE)
quantileResidualPlot(fit12gsr)

# Such StMAR(3,2) that the AR coefficients are restricted to be
# the same for both regimes and that the second AR coefficients are
# constrained to zero.
fit32rc <- fitGSMAR(logVIX, 3, 2, model="StMAR", restricted=TRUE,
 constraints=matrix(c(1, 0, 0, 0, 0, 1), ncol=2))
quantileResidualPlot(fit32rc)
# }