uGMAR (version 3.2.6)

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 {
# StMAR model
fit42 <- fitGSMAR(data=T10Y1Y, p=4, M=2, model="StMAR")
quantileResidualPlot(fit42)

# Restricted StMAR model: plot also the individual statistics with
# their approximate critical bounds using the given data
fit42r <- fitGSMAR(T10Y1Y, 4, 2, model="StMAR", restricted=TRUE)
quantileResidualPlot(fit42r)

# Non-mixture version of StMAR model
fit101t <- fitGSMAR(T10Y1Y, 10, 1, model="StMAR", ncores=1, ncalls=1)
quantileResidualPlot(fit101t)

# G-StMAR model
fit42g <- fitGSMAR(T10Y1Y, 4, M=c(1, 1), model="G-StMAR")
quantileResidualPlot(fit42g)

# Two-regime GMAR p=2 model with the second AR coeffiecient of
# of the second regime contrained to zero.
constraints <- list(diag(1, ncol=2, nrow=2), as.matrix(c(1, 0)))
fit22c <- fitGSMAR(T10Y1Y, 2, 2, constraints=constraints)
quantileResidualPlot(fit22c)
# }