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Seasonal decomposition with a modified Hodrick-Prescott filter

Author

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  • Buss, Ginters

Abstract

I describe preliminary results for seasonal decomposition procedure using a modified Hodrick-Prescott (Leser) filter. The procedure is simpler to implement compared to two currently most popular seasonal decomposition procedures - X-11 filters developed by the U.S. Census Bureau and SEATS developed by the Bank of Spain. A case study for Latvia's quarterly gross domestic product shows the procedure is able to extract a stable seasonal component, yet allowing for structural changes in seasonality.

Suggested Citation

  • Buss, Ginters, 2010. "Seasonal decomposition with a modified Hodrick-Prescott filter," MPRA Paper 24133, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:24133
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    File URL: https://mpra.ub.uni-muenchen.de/24133/1/MPRA_paper_24133.pdf
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    References listed on IDEAS

    as
    1. Regina Kaiser & Agustín Maravall, 2000. "Notes on Time Series Analysis, ARIMA Models and Signal Extraction," Working Papers 0012, Banco de España;Working Papers Homepage.
    2. Kaiser, Regina & Maravall, Agustín, 2000. "Notes on time serie analysis, ARIMA models and signal extraction," DES - Working Papers. Statistics and Econometrics. WS 10058, Universidad Carlos III de Madrid. Departamento de Estadística.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    seasonal decomposition; Hodrick-Prescott filter; quarterly GDP;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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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