Robust inference for threshold regression models

Javier Hidalgo, Jungyoon Lee, Myung Hwan Seo

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Abstract

This paper considers robust inference in threshold regression models when the practitioners do not know whether at the threshold point the true specification has a kink or a jump, nesting previous works that assume either continuity or discontinuity at the threshold. We find that the parameter values under the kink restriction are irregular points of the Hessian matrix, destroying the asymptotic normality and inducing the cube-root convergence rate for the threshold estimate. However, we are able to obtain the same asymptotic distribution as Hansen (2000) for the quasi-likelihood ratio statistic for the unknown threshold. We propose to construct confidence intervals for the threshold by bootstrap test inversion. Finite sample performances of the proposed procedures are examined through Monte Carlo simulations and an economic empirical application is given.
Original languageEnglish
Pages (from-to)291-309
Number of pages19
JournalJournal of Econometrics
Volume210
Issue number2
Early online date8 Feb 2019
DOIs
Publication statusPublished - Jun 2019

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