Forecasting inflation in Sudan /

This paper forecasts inflation in Sudan following two methodologies: the Autoregressive Moving Average (ARMA) model and by looking at the leading indicators of inflation. The estimated ARMA model remarkably tracks the actual inflation during the sample period. The Granger causality test suggests tha...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Moriyama, Kenji (Autor), Naseer, Abdul (Autor)
Autor Corporativo: International Monetary Fund. Middle East and Central Asia Department
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [Washington, D.C.] : International Monetary Fund, ©2009.
Colección:IMF working paper ; WP/09/132.
Temas:
Inflation (Finance) > Sudan > Forecasting.
Inflation > Soudan > Prévision.
Inflation (Finance) > Forecasting
Inflation.
Prognoseverfahren.
ARMA-Modell.
Kausalanalyse.
Sudan.
Sudan
Acceso en línea:Texto completo

MARC

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245 1 0 |a Forecasting inflation in Sudan /  |c prepared by Kenji Moriyama and Abdul Naseer. 
260 |a [Washington, D.C.] :  |b International Monetary Fund,  |c ©2009. 
300 |a 1 online resource (25 pages) :  |b color illustrations 
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504 |a Includes bibliographical references (page 25). 
588 0 |a Print version record. 
520 |a This paper forecasts inflation in Sudan following two methodologies: the Autoregressive Moving Average (ARMA) model and by looking at the leading indicators of inflation. The estimated ARMA model remarkably tracks the actual inflation during the sample period. The Granger causality test suggests that private sector credit and world wheat prices are the leading indicators explaining inflation in Sudan. Inflation forecasts based on both approaches suggest that inflationary pressures for 2009 and 2010 will be modest and that inflation will remain in single-digits, assuming that prudent macroecono. 
506 |3 Use copy  |f Restrictions unspecified  |2 star  |5 MiAaHDL 
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505 0 |a I. Introduction; II. Recent Developments; Figures; 1. Average and Standard Deviation of Inflation, 2000-08; 2. Monthly Inflation (12-Month) in Sudan, January 2000-October 2008; III. Methodology; A. Which Inflation Should Be Forecasted?; 3. Overall Inflation, 2000-08; B. Autoregressive Moving Average (ARMA) Model; 4. Cumulative Spectral Distribution of Inflation, 2000-08; C. Leading Indicators; 5. Currency Holding and Islamic Dummies, 2005-08; 6. Candidates of Leading Indicators; IV. Results; A. ARMA Model; 7. Actual and Projected Inflation Based on the Estimated ARMA Models 
505 8 |a B. Granger Causality Tests for Leading IndicatorsV. Implications-What Can be said from the Estimated Model and the Tests?; A. Can the Estimated Model Explain the Surge of Inflation in 2007 and 2008?; 8. Forecasted Inflation, July 2007-December 2008; 9. Forecast Errors of the Model and Bread Contribution to Inflation, July 2007-October 2008; B. Forecasting Inflation for 2009 and 2010; 10. Forecasted Inflation; 11. Inflation Forecast Based on ARMA (4,5), July 2008-December 2010; C. Leading Indicators (Private Sector Credit Growth and Wheat Price Inflation) 
505 8 |a 12. Oil Price Projections, World Economic Outlook, 2000-1013. Wheat Price Projections, World Economic Outlook, 2000-10; VI. Conclusions; Tables; 1. Estimated ARMA Model of Inflation; 2. Main Statistics of Various ARMA Models, 2000-08; 3. Granger Causality Tests Between Inflation and Leading Indicators, 2000-08; Appendices; I. The Schwartz Information Criterion; II. Estimated ARMA Model for main Monetary Aggregates; Appendix Tables; A1. Estimated ARMA Model of Broad money, 2000-08; References 
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650 0 |a Inflation (Finance)  |z Sudan  |x Forecasting. 
650 6 |a Inflation  |z Soudan  |x Prévision. 
650 7 |a Inflation (Finance)  |x Forecasting  |2 fast 
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650 7 |a Prognoseverfahren.  |2 stw 
650 7 |a ARMA-Modell.  |2 stw 
650 7 |a Kausalanalyse.  |2 stw 
650 7 |a Sudan.  |2 stw 
700 1 |a Naseer, Abdul,  |e author. 
710 2 |a International Monetary Fund.  |b Middle East and Central Asia Department. 
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