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Data-driven local polynomial for the trend and its derivatives in economic time series

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  • Yuanhua Feng
  • Thomas Gries
  • Marlon Fritz

Abstract

The main purpose of this paper is the development of data-driven iterative plug-in algorithms for local polynomial estimation of the trend and its derivatives under dependent errors. Furthermore, a data-driven lag-window estimator for the variance factor in the bandwidth is proposed so that the nonparametric stage is carried out without any parametric assumption on the stationary errors. Analysis of the residuals using an ARMA model is further discussed. Moreover, some computational features of the data-driven algorithms are discussed in detail. Practical performance of the proposals is confirmed by a simulation study and a comparative study, and illustrated by quarterly US GDP and labour force data. An R package called ‘smoots’ (smoothing time series) for smoothing the trend and its derivatives in short-memory time series is developed based on the proposals of this paper.

Suggested Citation

  • Yuanhua Feng & Thomas Gries & Marlon Fritz, 2020. "Data-driven local polynomial for the trend and its derivatives in economic time series," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 32(2), pages 510-533, April.
    • Handle: RePEc:taf:gnstxx:v:32:y:2020:i:2:p:510-533
    DOI: 10.1080/10485252.2020.1759598
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    Cited by:

    1. Sebastian Letmathe, 2022. "Data-driven P-Splines under short-range dependence," Working Papers CIE 152, Paderborn University, CIE Center for International Economics.
    2. Feng, Yuanhua & Härdle, Wolfgang Karl, 2020. "A data-driven P-spline smoother and the P-Spline-GARCH models," IRTG 1792 Discussion Papers 2020-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Xuehai Zhang, 2019. "A Box-Cox semiparametric multiplicative error model," Working Papers CIE 125, Paderborn University, CIE Center for International Economics.
    4. Xuehai Zhang, 2019. "A Box-Cox semiparametric multiplicative error model," Working Papers CIE 122, Paderborn University, CIE Center for International Economics.

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