Modelling the currency in circulation for the State of Qatar
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.
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Volume (Year): 5 (2012)
Issue (Month): 4 (December)
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References listed on IDEAS
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.:
- Francis X. Diebold & Robert S. Mariano, 1994.
"Comparing Predictive Accuracy,"
NBER Technical Working Papers
0169, National Bureau of Economic Research, Inc.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
- 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.
- Bindseil, Ulrich & Seitz, Franz, 2001. "The supply and demand for Eurosystem deposits - The first 18 months," Working Paper Series 0044, European Central Bank.
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