IDEAS home Printed from https://ideas.repec.org/p/bdi/opques/qef_835_24.html
   My bibliography  Save this paper

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

    Download full text from publisher

    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2024-0835/QEF_835_24.pdf
    Download Restriction: no
    ---><---

    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, January.
    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hall, Viv B & Thomson, Peter, 2022. "A boosted HP filter for business cycle analysis: evidence from New Zealand’s small open economy," Working Paper Series 9473, Victoria University of Wellington, School of Economics and Finance.
    2. Alexander Dokumentov & Rob J. Hyndman, 2022. "STR: Seasonal-Trend Decomposition Using Regression," INFORMS Joural on Data Science, INFORMS, vol. 1(1), pages 50-62, April.
    3. Viv B. Hall & Peter Thomson, 2021. "Does Hamilton’s OLS Regression Provide a “better alternative” to the Hodrick-Prescott Filter? A New Zealand Business Cycle Perspective," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(2), pages 151-183, November.
    4. Massimiliano Giacalone & Raffaele Mattera & Eugenia Nissi, 2020. "Economic indicators forecasting in presence of seasonal patterns: time series revision and prediction accuracy," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 67-84, February.
    5. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. Djuranovik, Leslie, 2014. "The Indonesian macroeconomy and the yield curve: A dynamic latent factor approach," Journal of Asian Economics, Elsevier, vol. 34(C), pages 1-15.
    7. Lahiri, Kajal & Yin, Yimeng, 2024. "Seasonality in U.S. disability applications, labor market, and the pandemic echoes," Labour Economics, Elsevier, vol. 87(C).
    8. Viv B Hall & Peter Thomson, 2020. "Does Hamilton’s OLS regression provide a “better alternative†to the Hodrick-Prescott filter? A New Zealand business cycle perspective," CAMA Working Papers 2020-71, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Alexander Dokumentov & Rob J. Hyndman, 2015. "STR: A Seasonal-Trend Decomposition Procedure Based on Regression," Monash Econometrics and Business Statistics Working Papers 13/15, Monash University, Department of Econometrics and Business Statistics.
    10. Naoto Kunitomo & Seisho Sato, 2015. "Trend, Seasonality and Economic Time Series:the Nonstationary Errors-in-variables Models," CIRJE F-Series CIRJE-F-977, CIRJE, Faculty of Economics, University of Tokyo.
    11. Viv B. Hall & Peter Thomson, 2022. "A boosted HP filter for business cycle analysis:evidence from New Zealand's small open economy," CAMA Working Papers 2022-45, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    12. Avanzi, Benjamin & Taylor, Greg & Vu, Phuong Anh & Wong, Bernard, 2020. "A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 50-71.
    13. Prilly Oktoviany & Robert Knobloch & Ralf Korn, 2021. "A machine learning-based price state prediction model for agricultural commodities using external factors," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1063-1085, December.
    14. David Bolder & Shudan Liu, 2007. "Examining Simple Joint Macroeconomic and Term-Structure Models: A Practitioner's Perspective," Staff Working Papers 07-49, Bank of Canada.
    15. Yuo-Hsien Shiau & Su-Fen Yang & Rishan Adha & Syamsiyatul Muzayyanah, 2022. "Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    16. Clements, Kenneth W. & Fry, Renée, 2008. "Commodity currencies and currency commodities," Resources Policy, Elsevier, vol. 33(2), pages 55-73, June.
    17. Faust, Jon & Gupta, Abhishek, 2010. "Posterior Predictive Analysis for Evaluating DSGE Models," MPRA Paper 26721, University Library of Munich, Germany.
    18. Giancarlo Bruno & Edoardo Otranto, 2006. "The choice of time interval in seasonal adjustment: A heuristic approach," Statistical Papers, Springer, vol. 47(3), pages 393-417, June.
    19. Rutger-Jan Lange & Coen N. Teulings, 2021. "The option value of vacant land: Don't build when demand for housing is booming," Tinbergen Institute Discussion Papers 21-022/IV, Tinbergen Institute.
    20. Matteo Barigozzi & Matteo Luciani, 2019. "Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm," Papers 1910.03821, arXiv.org, revised Sep 2024.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdi:opques:qef_835_24. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bdigvit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.