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Improving likelihood-ratio-based confidence intervals for threshold parameters in finite samples
Studies in Nonlinear Dynamics & Econometrics
AbstractWithin 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 adoi:10.1515/snde-2016-0084 fatcat:t7dhx3gfqjhtffa2y4vajtb7wu