
profile_logliks
plots profile log-likelihoods around the estimates.
profile_logliks(gsmar, scale = 0.02, nrows, ncols, precision = 200)
Only plots to a graphical device and doesn't return anything.
a class 'gsmar' object, typically generated by fitGSMAR
or GSMAR
.
a numeric scalar specifying the interval plotted for each estimate: the estimate plus-minus abs(scale*estimate)
.
how many rows should be in the plot-matrix? The default is max(ceiling(log2(nparams) - 1), 1)
.
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.
at how many points should each profile log-likelihood be evaluated at?
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.
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.
quantile_residual_plot
, diagnostic_plot
, cond_moment_plot
, GSMAR
,
quantile_residual_tests
, simulate.gsmar
# \donttest{
## 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)
# }
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