Modelling the Currency in Circulation for the State of Qatar
AbstractThe 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 20159.
Date of creation: 15 Jan 2010
Date of revision:
Currency in Circulation; Forecasting; Seasonal ARIMA;
Other versions of this item:
- 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, vol. 5(4), pages 321-339, December.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-03-26 (All new papers)
- NEP-CWA-2011-03-26 (Central & Western Asia)
- NEP-FOR-2011-03-26 (Forecasting)
- NEP-MON-2011-03-26 (Monetary Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Bindseil, Ulrich & Seitz, Franz, 2001. "The supply and demand for Eurosystem deposits - The first 18 months," Working Paper Series 0044, European Central Bank.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 20(1), pages 134-44, January.
- 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.
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