Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series

Pavel Cizek, Chao Koo

Research output: Working paperDiscussion paperOther research output

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

Keywords

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

Fingerprint Dive into the research topics of 'Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series'. Together they form a unique fingerprint.

Cite this