الفهرس 
Comparison and evaluation of a functional time series model for inflation forecasting in Uganda
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Abstract
Inflation is a major economic problem in emerging market economies and requires accurate models to avoid high volatility and long periods of inflation. This thesis is aimed at evaluating a Functional Time Series (FTS) model as compared to other models in forecasting inflation in Uganda. The monthly Time Series (TS) data for the general Consumer Price Index (CPI) was used during the period of Jul-2005 to Jun-2020. The ARIMA and SARIMA methodologies of Box and Jenkins are explored to evaluate the FTS model of forecasting the general CPI. The accuracies of the models are compared and validated using various accuracy measures, including MSE, AIC, and BIC criteria. Existing inflation models in Uganda are outdated by structural changes in the economy igniting the need for a novel accurate model for forecasting inflation. FTS technique is overall accurate and particularly used to model high-frequency data such as Uganda general CPI data modeled as a functional observation after smoothing the data by kernel smoothing methods compared to traditional methods. Business operations and consumers normally base their decisions on inflations modeled and forecasted. Their decisions are affected by inflation uncertainties that hinder their motivations to invest and save in a given country as they try to avoid inflation-related risks Findings show FTS having great accuracies with a relative error of the model almost 6 times better than SARIMA and over 10 times better than ARIMA. The model is therefore recommended as a prodigious model for forecasting Uganda inflation, opening a new framework for extending the Box and Jenkin{u2019}s methodology to the functional setting