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Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series

Author

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  • Cizek, Pavel

    (Tilburg University, Center For Economic Research)

  • Koo, Chao

    (Tilburg University, Center For Economic Research)

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.

Suggested Citation

  • Cizek, Pavel & Koo, Chao, 2017. "Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series," Discussion Paper 2017-017, Tilburg University, Center for Economic Research.
  • Handle: RePEc:tiu:tiucen:c849e96f-3ad1-461e-96c6-f095affc1053
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    References listed on IDEAS

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    More about this item

    Keywords

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

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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