Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series

Pavel Cizek, Chao Koo

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Abstract

An important and widely used class of semiparametric models is formed by the varyingcoefficient 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 heteroscedastic 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.
Original languageEnglish
Place of PublicationTilburg
PublisherCentER, Center for Economic Research
Number of pages75
Volume2017-017
Publication statusPublished - 22 Mar 2017

Publication series

NameCentER Discussion Paper
Volume2017-017

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Keywords

  • change point
  • Heteroscedasticity
  • local linear fitting
  • nonlinear time series
  • varying-coefficient models

Cite this

Cizek, P., & Koo, C. (2017). Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series. (CentER Discussion Paper; Vol. 2017-017). Tilburg: CentER, Center for Economic Research.