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A trend filtering method closely related to $$\ell _{1}$$ ℓ 1 trend filtering

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  • Hiroshi Yamada

    (Hiroshima University)

Abstract

The filtering method developed by Kim et al. (SIAM Rev 51:339–360, 2009), $$\ell _{1}$$ ℓ 1 trend filtering, is attractive because it enables us to estimate a continuous piecewise linear trend. This paper introduces a new filtering method closely related to $$\ell _{1}$$ ℓ 1 trend filtering in order to contribute to the accumulation of knowledge on $$\ell _{1}$$ ℓ 1 trend filtering. We show that the piecewise linearity, which is the key feature of $$\ell _{1}$$ ℓ 1 trend filtering, is derived from the new filtering. For this reason, we refer to the filtering as ‘pure’ $$\ell _{1}$$ ℓ 1 trend filtering. We also demonstrate some other miscellaneous results concerning the new filtering.

Suggested Citation

  • Hiroshi Yamada, 2018. "A trend filtering method closely related to $$\ell _{1}$$ ℓ 1 trend filtering," Empirical Economics, Springer, vol. 55(4), pages 1413-1423, December.
  • Handle: RePEc:spr:empeco:v:55:y:2018:i:4:d:10.1007_s00181-017-1349-8
    DOI: 10.1007/s00181-017-1349-8
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    References listed on IDEAS

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    More about this item

    Keywords

    $$ell _{1}$$ ℓ 1 trend filtering; Generalized lasso regression; Hodrick–Prescott filtering; Ridge regression; Penalized least squares; 1d fused lasso; Total variation denoising;
    All these keywords.

    JEL classification:

    • 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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