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Dual-trend and dual long-memory time series modelling

Author

Listed:
  • Shujie Li

    (Paderborn University)

  • Yuanhua Feng

    (Paderborn University)

Abstract

Many economic and financial series exhibit non-stationarity as well as long-memory behavior in both the first and second moments. To capture both non-stationarity and long-memory characteristics simultaneously, a general dual-trend and dual longmemory framework is proposed. In this framework, the error term of the semiparametric FARIMA model is assumed to exhibit a slowly changing scale and longmemory heteroskedasticity. A four-step estimation procedure is proposed, including a trend and a FARIMA model estimation for the first moment, followed by a scaling function and a long-memory volatility model estimation for the second moment. Three long-memory EGARCH-type models and the FIGARCH model are employed in the final stage. Our results indicate that the proposed approach can effectively model the selected economic series exhibiting dual-trend and dual long-memory features.

Suggested Citation

  • Shujie Li & Yuanhua Feng, 2026. "Dual-trend and dual long-memory time series modelling," Working Papers CIE 174, Paderborn University, CIE Center for International Economics.
    • Handle: RePEc:pdn:ciepap:174
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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