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

Author

Listed:
  • Yuanhua Feng

    (Paderborn University)

  • Thomas Gries

    (Paderborn University)

Abstract

The main purpose of this paper is the development of iterative plug-in algorithms for local polynomial estimation of the trend and its derivatives in macroeconomic time series. In particular, a data-driven lag-window estimator for the variance factor is proposed so that the bandwidth is selected without any parametric assumption on the stationary errors. Further analysis of the residuals using an ARMA model is discussed briefl y. Moreover, confidence bounds for the trend and its derivatives are conducted using some asymptotically unbiased estimates and applied to test possible linearity of the trend. These graphical tools also provide us further detailed features about the economic development. Practical performance of the proposals is illustrated by quarterly US and UK GDP data.

Suggested Citation

  • Yuanhua Feng & Thomas Gries, 2017. "Data-driven local polynomial for the trend and its derivatives in economic time series," Working Papers CIE 102, Paderborn University, CIE Center for International Economics.
    • Handle: RePEc:pdn:ciepap:102
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    File URL: http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/ciepap/WP102.pdf
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    Cited by:

    1. Xuehai Zhang, 2019. "A Box-Cox semiparametric multiplicative error model," Working Papers CIE 122, Paderborn University, CIE Center for International Economics.
    2. Sebastian Letmathe, 2022. "Data-driven P-Splines under short-range dependence," Working Papers CIE 152, Paderborn University, CIE Center for International Economics.
    3. 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".
    4. Xuehai Zhang, 2019. "A Box-Cox semiparametric multiplicative error model," Working Papers CIE 125, Paderborn University, CIE Center for International Economics.

    More about this item

    Keywords

    Macroeconomic time series; semiparametric modelling; nonparametric regression with dependent errors; bandwidth selection; misspecification test;
    All these keywords.

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