 Modelling and forecasting inflation in Uganda (2005-2014)William Lubowa, James Wokadala and Tom Makumbi Nyanzi International Journal of Sustainable Economy, 2017, vol. 9, issue 2, 121-141 Abstract: The study was set to fit appropriate auto-regressive integrated moving average (ARIMA) and vector auto-regressive (VAR) models for forecasting Uganda's core inflation; to compare their forecasting capabilities and establish whether the forecasts produced were significantly different. The study employed Box and Jenkins (1976) and Sims (1980) modelling techniques on monthly time series data for core inflation index, broad money supply (M2), nominal official exchange rate and short-term 91-day Treasury bill rates for the period July 2005 to December 2014. The appropriate ARIMA model was evaluated and found to be ARIMA(3, 1, 3). Pairwise Granger causality test confirmed that money supply, nominal Ushs/USD exchange rate and short-term Treasury bill rate caused core inflation. Thus, the appropriate restricted VAR (VECM) model was estimated and found to produce more accurate core inflation forecast for longer time horizons while ARIMA was better for shorter forecast horizons. However, the forecasts from both models were not significantly different. Keywords: inflation forecasting; auto-regressive integrated moving average; ARIMA; vector auto-regressive; VAR; Uganda; modelling. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijsuse:v:9:y:2017:i:2:p:121-141 Access Statistics for this article More articles in International Journal of Sustainable Economy from Inderscience Enterprises Ltd |
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