A numerical method to obtain exact confidence intervals for likelihood-based parameter estimators. (English) Zbl 07702197
Summary: We propose a numerical method for obtaining exact confidence intervals of likelihood-based parameter estimators for general multi-parameter models. Although the test inversion method provides exact confidence intervals, it is applicable only to single-parameter models. Our new method can be applied to general multi-parameter models without loss of accuracy, which is in sharp contrast to other multi-parameter extensions of the test inversion. Using Monte Carlo simulations, we show that our method is feasible and provides correct coverage probabilities in finite samples.
Keywords:
confidence interval; Neyman construction; test inversion; inequality constraint; finite sample distribution; financial time series modelSoftware:
fda (R)References:
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