uGMAR (version 3.2.6)

profile_logliks: Plot profile log-likehoods around the estimates

Description

profile_logliks plots profile log-likelihoods around the estimates.

Usage

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

Arguments

gsmar

object of class 'gsmar' created with the function 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.

precission

at how many points should each profile log-likelihood be evaluated at?

Value

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

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 precission when considering extremely large parameter values, say, overly large degrees freedom estimates.

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

quantileResidualPlot, diagnosticPlot, condmomentPlot, GSMAR, quantileResidualTests, simulateGSMAR

Examples

# NOT RUN {
# StMAR model
fit42 <- fitGSMAR(data=T10Y1Y, p=4, M=2, model="StMAR")
profile_logliks(fit42)

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

# G-StMAR model
fit42g <- fitGSMAR(T10Y1Y, 4, M=c(1, 1), model="G-StMAR")
profile_logliks(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)
profile_logliks(fit22c)
# }