A smoothed least squares estimator for threshold regression models

Oliver Linton and Myung Hwan Seo

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We propose a smoothed least squares estimator of the parameters of a threshold regression model. Our model generalizes that considered in Hansen (2000) to allow the thresholding to depend on a linear index of observed regressors, thus allowing discrete variables to enter. We also do not assume that the threshold e¤ect is vanishingly small. Our estimator is shown to be consistent and asymptotically normal thus facilitating standard inference techniques based on estimated standard errors or standard bootstrap for the threshold parameters themselves. We compare our con dence intervals with those of Hansen (2000) in a simulation study and show that our methods outperform his for large values of the threshold. We also include an application to cross-country growth regressions.

Keywords: Index model; Sample Splitting; Segmented Regression; Smoothing; Threshold Estimation. (search for similar items in EconPapers)
JEL-codes: C12 C13 C14 (search for similar items in EconPapers)
Pages: 50 pages
Date: 2005-10
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://eprints.lse.ac.uk/4434/ Open access version. (application/pdf)

Related works:
Journal Article: A smoothed least squares estimator for threshold regression models (2007)
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:ehl:lserod:4434

Access Statistics for this paper

More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager (lseresearchonline@lse.ac.uk).