Jump-preserving varying-coefficient models for nonlinear time series

Pavel Cizek and Chao Hui Koo

Econometrics and Statistics, 2021, vol. 19, issue C, 58-96

Abstract: 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.

Keywords: Asymptotics; Discontinuity; Heteroskedasticity; Local linear fitting; Nonlinear time series; Varying-coefficient models (search for similar items in EconPapers)
JEL-codes: C13 C14 C22 (search for similar items in EconPapers)
Date: 2021
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Related works:
Working Paper: Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series (2017)
Working Paper: Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:19:y:2021:i:c:p:58-96

DOI: 10.1016/j.ecosta.2020.04.005

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