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Modelling the Currency in Circulation for the State of Qatar

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  • Balli, Faruk
  • Elsamadisy, Elsayed

Abstract

The main concern of this report is to model the daily and weekly forecasting of the currency in circulation (CIC) for the State of Qatar. The time series of daily observations of the CIC is expected to display marked seasonal and cyclical patterns daily, weekly or even monthly basis. We have compared the forecasting performance of typical linear forecasting models, namely the regression model and the seasonal ARIMA model using daily data. We found that seasonal ARIMA model performs better in forecasting CIC, particularly for short-term horizons.

Suggested Citation

  • Balli, Faruk & Elsamadisy, Elsayed, 2010. "Modelling the Currency in Circulation for the State of Qatar," MPRA Paper 20159, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:20159
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    References listed on IDEAS

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    1. Alberto Cabrero & Gonzalo Camba-Mendez & Astrid Hirsch & Fernando Nieto, 2009. "Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(3), pages 194-217.
    2. Naoto Kunitomo & Makoto Takaoka, 2002. "On RegARIMA Model, RegSSARMA Model and Seasonality," CIRJE F-Series CIRJE-F-146, CIRJE, Faculty of Economics, University of Tokyo.
    3. 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.
    4. Riaz Riazuddin & Mahmood ul Hasan Khan, 2005. "Detection and Forecasting of Islamic Calendar Effects in Time series Data," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 1, pages 25-34.
    5. 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.
    6. Unknown, 2005. "Forward," 2005 Conference: Slovenia in the EU - Challenges for Agriculture, Food Science and Rural Affairs, November 10-11, 2005, Moravske Toplice, Slovenia 183804, Slovenian Association of Agricultural Economists (DAES).
    7. William A Allen, 2004. "Implementing Monetary Policy," Lectures, Centre for Central Banking Studies, Bank of England, number 4, April.
    8. Maroje Lang & Davor Kunovac & Silvio Basač & Željka Štaudinger, 2008. "Modelling of Currency outside Banks in Croatia," Working Papers 17, The Croatian National Bank, Croatia.
    9. Bindseil, Ulrich & Seitz, Franz, 2001. "The supply and demand for Eurosystem deposits - The first 18 months," Working Paper Series 44, European Central Bank.
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    More about this item

    Keywords

    Currency in Circulation; Forecasting; Seasonal ARIMA;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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