A numerical method to obtain exact confidence intervals for likelihood-based parameter estimators

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. (c) 2022 Elsevier B.V. All rights reserved.