Improving Likelihood-Ratio-Based Confidence Intervals for Threshold Parameters in Finite Samples
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 approach 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 relatively 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..
|Date of creation:||Mar 2014|
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