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Modelling the currency in circulation for the State of Qatar

Author

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  • Faruk Balli
  • Elsayed Mousa Elsamadisy

Abstract

Purpose - This paper seeks to model the daily and weekly forecasting of the currency in circulation (CIC) for the State of Qatar. Design/methodology/approach - The paper employs linear forecasting models, the regression model and the seasonal ARIMA model to forecast the CIC for Qatar. Findings - Comparing the linear methods, the seasonal ARIMA model provides better estimates for short‐term forecasts. The range of forecast errors for the seasonal ARIMA model forecasts are less than 100 million QR for the short‐term CIC forecasts. Practical implications - The findings of this paper suggest that the CIC in Qatar is in a pattern and it would be easier to forecast the currency in circulation in Qatar economy. Accurate estimates of money market liquidity would help Qatar Central bank, to maintain the price stability in the Qatar economy. Originality/value - This paper forecasts the currency in circulation for the State of Qatar. Additionally, the empirical part of the paper compares the different methodologies find the appropriate model for the CIC for the state of Qatar.

Suggested Citation

  • Faruk Balli & Elsayed Mousa Elsamadisy, 2012. "Modelling the currency in circulation for the State of Qatar," International Journal of Islamic and Middle Eastern Finance and Management, Emerald Group Publishing Limited, vol. 5(4), pages 321-339, November.
    • Handle: RePEc:eme:imefmp:v:5:y:2012:i:4:p:321-339
    DOI: 10.1108/17538391211282827
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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, Research and Statistics Department.
    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. William A Allen, 2004. "Implementing Monetary Policy," Lectures, Centre for Central Banking Studies, Bank of England, number 4, April.
    7. Maroje Lang & Davor Kunovac & Silvio Basač & Željka Štaudinger, 2008. "Modelling of Currency outside Banks in Croatia," Working Papers 17, The Croatian National Bank, Croatia.
    8. 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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    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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