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Estimation and inference of threshold regression models with measurement errors

Terence Tai Leung Chong (), Chen Haiqiang, Tsz-Nga Wong () and Yan Isabel Kit-Ming
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Chen Haiqiang: Wang Yanan Institute for Studies in Economics, Xiamen University , Xiamen, Fujian, China
Yan Isabel Kit-Ming: Department of Economics and Finance, City University of Hong Kong, Hong Kong, China

Studies in Nonlinear Dynamics & Econometrics, 2018, vol. 22, issue 2, 16

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: Hausman-type test; measurement error; threshold model (search for similar items in EconPapers)
JEL-codes: C12 C22 (search for similar items in EconPapers)
Date: 2018
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Working Paper: Estimation and Inference of Threshold Regression Models with Measurement Errors (2015) Downloads
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