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Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank

Author

Listed:
  • Cabrero, Alberto
  • Camba-Méndez, Gonzalo
  • Hirsch, Astrid
  • Nieto, Fernando

Abstract

The main focus of this paper is to model the daily series of banknotes in circulation in the context of the liquidity management of the Eurosystem. The series of banknotes in circulation displays very marked seasonal patterns. To the best of our knowledge the empirical performance of two competing approaches to model seasonality in daily time series, namely the ARIMA-based approach and the Structural Time Series approach, has never been put to the test. The application presented in this paper provides valid intuition on the merits of each approach. The forecasting performance of the models is also assessed in the context of their impact on the liquidity management of the Eurosystem. JEL Classification: C22, C51, C53, C59

Suggested Citation

  • Cabrero, Alberto & Camba-Méndez, Gonzalo & Hirsch, Astrid & Nieto, Fernando, 2002. "Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank," Working Paper Series 0142, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20020142
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    File URL: http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp142.pdf
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    References listed on IDEAS

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    1. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    2. Harvey, Andrew & Koopman, Siem Jan & Riani, Marco, 1997. "The Modeling and Seasonal Adjustment of Weekly Observations," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 354-368, July.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    5. Pierce, David A & Grupe, Michael R & Cleveland, William P, 1984. "Seasonal Adjustment of the Weekly Monetary Aggregates: A Model-based Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 260-270, July.
    6. Siem Jan Koopman & Marius Ooms, 2003. "Time Series Modelling of Daily Tax Revenues," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(4), pages 439-469.
    7. Bindseil, Ulrich & Seitz, Franz, 2001. "The supply and demand for Eurosystem deposits - The first 18 months," Working Paper Series 0044, European Central Bank.
    8. Bindseil, Ulrich, 2001. "Central bank forecasts of liquidity factors: Quality, publication and the control of the overnight rate," Working Paper Series 0070, European Central Bank.
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    Citations

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    Cited by:

    1. Deinhammer, Harald & Ladi, Anna, 2017. "Modelling euro banknote quality in circulation," Occasional Paper Series 204, European Central Bank.
    2. Ioana Florentina SAVU, 2011. "Developing Partnership between National Bank of Romania and Universities," REVISTA DE MANAGEMENT COMPARAT INTERNATIONAL/REVIEW OF INTERNATIONAL COMPARATIVE MANAGEMENT, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 12(3), pages 565-574, July.
    3. Martin-Rodriguez, Gloria & Caceres-Hernandez, Jose Juan, 2012. "Forecasting weekly Canary tomato exports from annual surface data," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126364, International Association of Agricultural Economists.
    4. Diego Elías & Matías Vicens, 2012. "Bills and Coins Daily Demand Forecast," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(65-66), pages 23-39, September.
    5. Antonio Noriega & Carlos Capistrán & Manuel Ramos-Francia, 2013. "On the dynamics of inflation persistence around the world," Empirical Economics, Springer, vol. 44(3), pages 1243-1265, June.
    6. Ioana Florentina SAVU, 2011. "National Bank of Romania Management in Time of Financial Crisis," REVISTA DE MANAGEMENT COMPARAT INTERNATIONAL/REVIEW OF INTERNATIONAL COMPARATIVE MANAGEMENT, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 12(5), pages 1013-1021, December.
    7. Seitz, Franz & Fischer, Björn & Köhler, Petra, 2004. "The demand for euro area currencies: past, present and future," Working Paper Series 330, European Central Bank.
    8. Bindseil, Ulrich & Nyborg, Kjell G., 2007. "Monetary policy implementation: A European Perspective," Discussion Papers 2007/10, Norwegian School of Economics, Department of Business and Management Science.
    9. Bindseil, Ulrich & Camba-Mendez, Gonzalo & Hirsch, Astrid & Weller, Benedict, 2006. "Excess reserves and the implementation of monetary policy of the ECB," Journal of Policy Modeling, Elsevier, vol. 28(5), pages 491-510, July.
    10. Bukhari, Syed Kalim Hyder & Abdul, Jalil & Rao, Nasir Hamid, 2011. "Detection and Forecasting of Islamic Calendar Effects in Time Series Data: Revisited," MPRA Paper 31124, University Library of Munich, Germany.
    11. Marek Hlavacek & Michael Konak & Josef Cada, 2005. "The Application of Structured Feedforward Neural Networks to the Modelling of Daily Series of Currency in Circulation," Working Papers 2005/11, Czech National Bank, Research Department.
    12. Assenmacher, Katrin & Seitz, Franz & Tenhofen, Jörn, 2017. "The use of large denomination banknotes in Switzerland," International Cash Conference 2017 – War on Cash: Is there a Future for Cash? 162917, Deutsche Bundesbank.
    13. Erica Rizziato, 2010. "La Formazione-sviluppo per la creazione di moderne comunità lavorative [Developmnt-training to create working communities]," CERIS Working Paper 201003, Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY -NOW- Research Institute on Sustainable Economic Growth - Moncalieri (TO) ITALY.

    More about this item

    Keywords

    Daily Forecast; liquidity management; seasonality; time series models;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other

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