profile_logliks: Plot profile log-likelihoods around the estimates

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

Plot profile log-likelihoods around the estimates

Description

profile_logliks plots profile log-likelihoods around the estimates.

Usage

profile_logliks(gsmar, scale = 0.02, nrows, ncols, precision = 200)

Arguments

gsmar	a class 'gsmar' object, typically generated by fitGSMAR or GSMAR.
scale	a numeric scalar specifying the interval plotted for each estimate: the estimate plus-minus abs(scale*estimate).
nrows	how many rows should be in the plot-matrix? The default is max(ceiling(log2(nparams) - 1), 1).
ncols	how many columns should be in the plot-matrix? The default is ceiling(nparams/nrows). Note that nrows*ncols should not be smaller than the number of parameters.
precision	at how many points should each profile log-likelihood be evaluated at?

Details

The red vertical line points the estimate.

Be aware that the profile log-likelihood function is subject to a numerical error due to limited float-point precision when considering extremely large parameter values, say, overly large degrees freedom estimates.

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(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

quantile_residual_plot, diagnostic_plot, cond_moment_plot, GSMAR, quantile_residual_tests, simulate.gsmar

Examples

## The below examples the approximately 15 seconds to run.

# G-StMAR model with one GMAR type and one StMAR type regime
fit42gs <- fitGSMAR(M10Y1Y, p=4, M=c(1, 1), model="G-StMAR",
                    ncalls=1, seeds=4)
profile_logliks(fit42gs)

# GMAR model, graphs zoomed in closer.
fit12 <- fitGSMAR(data=simudata, p=1, M=2, model="GMAR", ncalls=1, seeds=1)
profile_logliks(fit12, scale=0.001)

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