Testing for Smooth Structural Changes in Time Series Models via Nonparametric Regression

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Estimating Smooth Structural Change in Cointegration Models (2017, unknown): “Nonparametric inference about time-varying parameters has received attention for modeling stationary or locally stationary time series data - see, for instance, Robinson (1989), Cai (2007), Li et al (2011), Chen and Hong (2012), and Zhang and Wu (2012).”
Non‐parametric detection and estimation of structural change (2012, unknown): “Chen and Hong (2012) in a number of directions: First, we develop estimators and tests under the hypothesis of time invariance for any given subset of the regression coefficients.”
Inference of time-varying regression models (2012, background): “Our setting is much more general than the one in [8] where (xi , ei) is assumed to be β-mixing and stationary.”
Detecting big structural breaks in large factor models (2014, methodology): “For instance, when the break date is not known as a priori, one can use the CUSUM type-test first proposed by Brown, Durbin and Evans (1975), and also Chen and Hong’s (2012) test via nonparametric regression.”
On Time-Varying Factor Models: Estimation and Testing ∗ (2017, methodology): “…and estimate the model via a Gibbs sampling procedure. nonparametric literature on time-varying models (see, e.g., Cai 2007, Robinson 2012, Chen and Hong 2012, Chen et al. 2012, Su et al. 2015) and model as a nonrandom function of : = ( ) where (·) is an unknown piece-wise smooth…”