Estimation and Inference of Threshold Regression Models with Measurement Errors

Terence Tai Leung Chong, Haiqiang Chen, Russell Wong and Isabel K. Yan

MPRA Paper from University Library of Munich, Germany

Abstract: An important assumption underlying standard threshold regression models and their variants in the extant literature is that the threshold variable is perfectly measured. Such an assumption is crucial for consistent estimation of model parameters. This paper provides the first theoretical framework for the estimation and inference of threshold regression models with measurement errors. A new estimation method that reduces the bias of the coefficient estimates and a Hausman-type test to detect the presence of measurement errors are proposed. Monte Carlo evidence is provided and an empirical application is given.

Keywords: Threshold Model; Measurement Error; Hausman-type Test. (search for similar items in EconPapers)
JEL-codes: C12 C22 (search for similar items in EconPapers)
Date: 2015-11-05
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Estimation and inference of threshold regression models with measurement errors (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:68457

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