Estimation and inference of threshold regression models with measurement errors
Terence Tai Leung Chong,
Chen Haiqiang,
Russell Wong and
Yan Isabel Kit-Ming
Additional contact information
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
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://doi.org/10.1515/snde-2014-0032 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.
Related works:
Working Paper: Estimation and Inference of Threshold Regression Models with Measurement Errors (2015) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bpj:sndecm:v:22:y:2018:i:2:p:16:n:1
Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/snde/html
DOI: 10.1515/snde-2014-0032
Access Statistics for this article
Studies in Nonlinear Dynamics & Econometrics is currently edited by Bruce Mizrach
More articles in Studies in Nonlinear Dynamics & Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().