Within the context of threshold regressions, we show that asymptotically-valid likelihood-ratio-based conﬁdence intervals for threshold parameters perform poorly in ﬁnite samples when the threshold eﬀect is large. A large threshold eﬀect leads to a poor approximation of the proﬁle likelihood in ﬁnite samples such that the conventional ap-proach to constructing conﬁdence intervals excludes the true threshold parameter value too often, resulting in low coverage rates. We propose a conservative modiﬁcation to the standard likelihood-ratio-based conﬁdence interval that has coverage rates at least as high as the nominal level, while still being informative in the sense of including rela-tively few observations of the threshold variable. An application to thresholds for U.S. industrial production growth at a disaggregated level shows the empirical relevance of applying the proposed approach.
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