A Smoothed Least Squares Estimator for Threshold Regression Models

By Myung Hwan Seo, Oliver B. Linton. 2007

164 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/811f9e0b6b4a397080bd50e31ed514f5a2c0b32c

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Citations in the corpus

STRUCTURAL THRESHOLD REGRESSION (2016, methodology): “Our research is related to several recent papers in the literature; see for example Hansen (2000), Caner and Hansen (2004), Seo and Linton (2007), Gonzalo and Wolf (2005), and Yu (2012, 2013a).”
Nested sub-sample search algorithm for estimation of threshold models (2016, background): “Other significant results related to threshold models include Tsay (1989, 1998), Hansen (1996), Caner and Hansen (2001), Gonzalo and Wolf (2005), Seo and Linton (2007), and Yu (2012), among others.”
On the least squares estimation of multiple-regime threshold autoregressive models (2012, background): “Seo and Linton (2007) proposed a smoothed LSE for a threshold regression model.”
ESTIMATION OF NONLINEAR ERROR CORRECTION MODELS (2010, unknown): “These assumptions are analogous to those imposed in Seo and Linton (2006) that study the SLS estimator of the threshold regression model.”
Robust inference for threshold regression models (2017, background): “On the other hand, it is worth mentioning that Seo and Linton (2007) considered the smoothed least squares estimator for the same setup.”
Dynamic panels with threshold effect and endogeneity (2016, unknown): “The standard least squares approach, such as Hansen (2000) and Seo and Linton (2007), requires exogeneity in all the covariates.”
Threshold autoregression in economics (2011, methodology): “As an alternative, Seo and Linton [74] propose a smoothed least squares estimator which achieves an asymptotic normal distribution and produces confidence intervals with excellent coverage performance.”
Threshold cointegration: overview and implementation in R (2013, methodology): “Similarly as in his previous work in the univariate case (Seo and Linton 2007), Seo considers a smoothed-LS estimator and ﬁnds that is converging at a slower rate but then normally distributed, allowing to obtain conﬁdence intervals.”
Threshold Regression With a Threshold Boundary (2020, methodology): “When the threshold is constant, the simulation studies in Yu (2015) show that the performance of their CI for γ is not as good as Hansen’s LR-CI or our NPI, while those in Seo and Linton (2007) show that their CI outperforms Hansen’s LR-CI for large threshold effects.”
Asymmetric Capital Structure Adjustments: New Evidence from Dynamic Panel Threshold Models (2012, background): “However, Seo and Linton (2007) note that in finite samples, the confidence interval for ĉ, denoted Cα, is not necessarily a true interval.”
Regression Kink With an Unknown Threshold (2017, methodology): “For inference on the regression parameters, relevant articles include Chan (1993), Hansen (2000), and Seo and Linton (2007).”