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A comparison of methods for trend estimation

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  • Marco Bianchi
  • Martin Boyle
  • Deirdre Hollingsworth

Abstract

This paper analyses a number of methods for trend estimation focusing on their ability to pick up turning points quickly at the end of a series. An application to the Bank of England flows M4 series is provided which shows that some of the proposed methods may be more reliable than others for this task.

Suggested Citation

  • Marco Bianchi & Martin Boyle & Deirdre Hollingsworth, 1999. "A comparison of methods for trend estimation," Applied Economics Letters, Taylor & Francis Journals, vol. 6(2), pages 103-109.
    • Handle: RePEc:taf:apeclt:v:6:y:1999:i:2:p:103-109
    DOI: 10.1080/135048599353726
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    Cited by:

    1. Elena Barton & Basad Al-Sarray & Stéphane Chrétien & Kavya Jagan, 2018. "Decomposition of Dynamical Signals into Jumps, Oscillatory Patterns, and Possible Outliers," Mathematics, MDPI, vol. 6(7), pages 1-13, July.
    2. Ilya Kuzminov & Pavel Bakhtin & Elena Khabirova & Irina V. Loginova, 2018. "Detecting and Validating Global Technology Trends Using Quantitative and Expert-Based Foresight Techniques," HSE Working papers WP BRP 82/STI/2018, National Research University Higher School of Economics.
    3. Zhao, Shan & Wei, G. W., 2003. "Jump process for the trend estimation of time series," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 219-241, February.
    4. Hua Xiangzhou & Hasan Nurul Ain Mohd & Costa Feroz De & Qiao Weihua, 2024. "Opportunities or Challenges? The Interplay between Artificial Intelligence and Corporate Social Responsibility Communication," Business Systems Research, Sciendo, vol. 15(1), pages 131-157.

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