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010   $a9786612843464
010   $a1-282-84346-X
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100   $a20100902d2009      uf             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aForecasting inflation in Sudan$b[electronic resource] /$fKenji Moriyama and Abdul Naseer
210   $a[Washington, D.C.] $cInternational Monetary Fund$d2009
215   $a1 online resource (27 p.)
225 1 $aIMF working paper ;$vWP/09/132
300   $aDescription based upon print version of record.
311   $a1-4519-1708-2 
320   $aIncludes bibliographical references.
327   $aContents; 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
327   $aB. 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)
327   $a12. 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
330   $aThis paper forecasts inflation in Sudan following two methodologies: the Autoregressive Moving Average (ARMA) model and by looking at the leading indicators of inflation. The estimated ARMA model remarkably tracks the actual inflation during the sample period. The Granger causality test suggests that private sector credit and world wheat prices are the leading indicators explaining inflation in Sudan. Inflation forecasts based on both approaches suggest that inflationary pressures for 2009 and 2010 will be modest and that inflation will remain in single-digits, assuming that prudent macroecono
410  0$aIMF working paper ;$vWP/09/132.
606   $aInflation (Finance)$zSudan
606   $aEconomic forecasting$zSudan
608   $aElectronic books.
615  0$aInflation (Finance)
615  0$aEconomic forecasting
700   $aMoriyama$b Kenji$0872379
712 02$aInternational Monetary Fund.
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910464006503321
996   $aForecasting inflation in Sudan$91947654
997   $aUNINA