Jump-preserving varying-coefficient models for nonlinear time series

Pavel Cizek, C.H. Koo

Research output: Contribution to journalArticleScientificpeer-review


An important and widely used class of semiparametric models is formed by the varying-coefficient models. Although the varying coefficients are traditionally assumed to be smooth functions, the varying-coefficient model is considered here with the coefficient functions containing a finite set of discontinuities. Contrary to the existing nonparametric and varying-coefficient estimation of piecewise smooth functions, the varying-coefficient models are considered here under dependence and are applicable in time series with heteroskedastic and serially correlated errors. Additionally, the conditional error variance is allowed to exhibit discontinuities at a finite set of points too. The (uniform) consistency and asymptotic normality of the proposed estimators are established and the finite-sample performance is tested via a simulation study and in a real-data example.
Original languageEnglish
Pages (from-to)58-96
JournalEconometrics and Statistics
Publication statusPublished - Jul 2021


  • asymptotics
  • discontinuity
  • heteroskedasticity
  • local linear fitting
  • nonlinear time series
  • varying-coefficient models


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