Improving likelihood-ratio-based confidence intervals for threshold parameters in finite samples

Donayre, Luiggi and Eo, Yunjong and Morley, James (2018) Improving likelihood-ratio-based confidence intervals for threshold parameters in finite samples. Studies in Nonlinear Dynamics and Econometrics, 22 (1). pp. 1-11. ISSN 1558-3708

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Abstract

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 US industrial production growth at a disaggregated level shows the empirical relevance of applying the proposed approach.

Item Type:
Journal Article
Journal or Publication Title:
Studies in Nonlinear Dynamics and Econometrics
Subjects:
ID Code:
130426
Deposited By:
Deposited On:
21 May 2019 08:40
Refereed?:
Yes
Published?:
Published
Last Modified:
05 Jul 2020 06:53