Accepted author manuscript, 608 KB, PDF document
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Improving likelihood-ratio-based confidence intervals for threshold parameters in finite samples
AU - Donayre, Luiggi
AU - Eo, Yunjong
AU - Morley, James
PY - 2018/2/1
Y1 - 2018/2/1
N2 - 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.
AB - 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.
KW - confidence interval
KW - finite-sample inference
KW - inverted likelihood ratio
KW - threshold regression
U2 - 10.1515/snde-2016-0084
DO - 10.1515/snde-2016-0084
M3 - Journal article
VL - 22
SP - 1
EP - 11
JO - Studies in Nonlinear Dynamics and Econometrics
JF - Studies in Nonlinear Dynamics and Econometrics
SN - 1558-3708
IS - 1
ER -