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

Luiggi Donayre, Yunjong Eo and James Morley

Studies in Nonlinear Dynamics & Econometrics, 2018, vol. 22, issue 1, 11

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.

Keywords: confidence interval; finite-sample inference; inverted likelihood ratio; threshold regression (search for similar items in EconPapers)
JEL-codes: C13 C20 (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://doi.org/10.1515/snde-2016-0084 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
Working Paper: Improving Likelihood-Ratio-Based Confidence Intervals for Threshold Parameters in Finite Samples (2014)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sndecm:v:22:y:2018:i:1:p:11:n:4

Ordering information: This journal article can be ordered from
https://www.degruyter.com/view/j/snde

DOI: 10.1515/snde-2016-0084

Access Statistics for this article

Studies in Nonlinear Dynamics & Econometrics is currently edited by Bruce Mizrach

More articles in Studies in Nonlinear Dynamics & Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().