Robust inference for threshold regression models

Hidalgo, Javier, Lee, Jungyoon and Seo, Myung Hwan (2019) Robust inference for threshold regression models. Journal of Econometrics, 210 (2). pp. 291-309. ISSN 0304-4076

Text (JoE_main) - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB)

• Author
• Author

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.

Item Type:	Article
Additional Information:	© 2019 Elsevier B.V.
Divisions:	Economics
Subjects:	H Social Sciences > HB Economic Theory
JEL classification:	C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C12 - Hypothesis Testing C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C24 - Truncated and Censored Models
Date Deposited:	28 Mar 2019 00:13
Last Modified:	12 Dec 2024 01:42
URI:	http://eprints.lse.ac.uk/id/eprint/100333

Actions (login required)

View Item