Robust inference for threshold regression models

By J. Hidalgo, Jungyoon Lee, M. Seo. Journal of Econometrics, 2017

64 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2c30571eb42dd0f29c3a23d02e2f72a091d33950

Citations in the corpus

• Estimation of dynamic panel threshold model using Stata (2019, unknown): “This is in contrast to the least squares estimator for the linear regression, for which Hidalgo et al. (2019) have shown that the cube root phenomenon appears.”