Likelihood estimation and inference in threshold regression

Ping Yu

Journal of Econometrics, 2012, vol. 167, issue 1, 274-294

Abstract: This paper studies likelihood-based estimation and inference in parametric discontinuous threshold regression models with i.i.d. data. The setup allows heteroskedasticity and threshold effects in both mean and variance. By interpreting the threshold point as a “middle” boundary of the threshold variable, we find that the Bayes estimator is asymptotically efficient among all estimators in the locally asymptotically minimax sense. In particular, the Bayes estimator of the threshold point is asymptotically strictly more efficient than the left-endpoint maximum likelihood estimator and the newly proposed middle-point maximum likelihood estimator. Algorithms are developed to calculate asymptotic distributions and risk for the estimators of the threshold point. The posterior interval is proved to be an asymptotically valid confidence interval and is attractive in both length and coverage in finite samples.

Keywords: Threshold regression; Structural change; Nonregular models; Boundary; Efficiency bounds; Bayes; Middle-point MLE; Compound Poisson process; Wiener–Hopf equation; Local asymptotic minimax; Credible set (search for similar items in EconPapers)
JEL-codes: C11 C13 C21 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:167:y:2012:i:1:p:274-294

DOI: 10.1016/j.jeconom.2011.12.002

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