Robust Estimation and Inference for Threshold Models with Integrated Regressors

45 Pages Posted: 1 Jul 2013

See all articles by Haiqiang Chen

Haiqiang Chen

Xiamen University - Wang Yanan Institute for studies in Economics

Date Written: June 30, 2013

Abstract

This paper studies the robust estimation and inference of threshold models with integrated regressors. We derive the asymptotic distribution of the profiled least squares (LS) estimator under the diminishing threshold effect assumption that the size of the threshold effect converges to zero. Depending on how rapidly this sequence converges, the model may be identified or only weakly identified and asymptotic theorems are developed for both cases. As the convergence rate is unknown in practice, a model-selection procedure is applied to determine the model identification strength and to construct robust confidence intervals, which have the correct asymptotic size irrespective of the magnitude of the threshold effect. The model is then generalized to incorporate endogeneity and serial correlation in error terms, under which, we design a Cochrane-Orcutt feasible generalized least squares (FGLS) estimator which enjoys efficiency gains and robustness against different error specifications, including both I(0) and I(1) errors. Based on this FGLS estimator, we further develop a sup-Wald statistic to test for the existence of the threshold effect. Monte Carlo simulations show that our estimators and test statistics perform well.

Keywords: Threshold effects, Integrated processes, Nonlinear cointegration, Weak identification

JEL Classification: C12, C22, C52

Suggested Citation

Chen, Haiqiang, Robust Estimation and Inference for Threshold Models with Integrated Regressors (June 30, 2013). Available at SSRN: https://ssrn.com/abstract=2287442 or http://dx.doi.org/10.2139/ssrn.2287442

Haiqiang Chen (Contact Author)

Xiamen University - Wang Yanan Institute for studies in Economics ( email )

Economics Building A307
Xiamen University
Xiamen, Fujian 361005
China

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