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Seasonal adjustment of credit time series in the Bank of Italy

Author

Listed:
  • Simone di Paolo

    (Bank of Italy)

  • Danilo Liberati

    (Bank of Italy)

Abstract

This paper presents the seasonal adjustment process applied to the credit time series produced by the Bank of Italy. Due to the methodological and computational improvements made in recent times, making the most suitable choice for estimating the seasonal component has become more challenging. First, the paper discusses the seasonal adjustment methods most widely adopted within the community of central banks and statistical institutes. Second, it compares the output of the different approaches with reference to the credit time series currently published by the Bank of Italy and explains the main reasons underlying the adoption of X-13ARIMA-SEATS. Finally, the paper extends the new approach in order to derive new seasonally adjusted time series for banks’ loans to non-financial corporations and to households classified by economic sector of activity (NACE) and by purpose of lending respectively.

Suggested Citation

  • Simone di Paolo & Danilo Liberati, 2024. "Seasonal adjustment of credit time series in the Bank of Italy," Questioni di Economia e Finanza (Occasional Papers) 835, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_835_24
    as

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    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2024-0835/QEF_835_24.pdf
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    References listed on IDEAS

    as
    1. Barend Abeln & Jan P. A. M. Jacobs, 2023. "COVID-19 and Seasonal Adjustment," SpringerBriefs in Economics, in: Seasonal Adjustment Without Revisions, chapter 0, pages 53-61, Springer.
    2. Hirotugu Akaike, 1980. "Seasonal Adjustment By A Bayesian Modeling," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 1-13, January.
    3. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    4. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, September.
    5. Olivier Darné & Laurent Ferrara & Dominique Ladiray, 2018. "A Brief History of Seasonal Adjustment Methods and Software Tools," Post-Print hal-03754072, HAL.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    seasonal adjustment; X-13ARIMA-SEATS; TRAMO-SEATS; banking data;
    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
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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