Abstract
This paper uses Box-Jenkins approach to model and forecast real GDP growth in Ethiopia. Such an approach could easily provide forecast for key macroeconomic variables in limited data environment. Based on the approach, the paper estimates Autoregressive Integrated Moving Average ARIMA (1,1,1) model and forecasts real GDP growth. Both the in-sample fit and pseudo-out of sample forecasts show that the ARIMA model’s performance are good and better than other forecasts.
Highlights
Economic forecasting is a common practice in economics
The main aim of this study is to show the use of univariate time series model for forecasting in countries with limited data environment
This paper aims to show the use of univariate time series model for forecasting in countries with limited data environment
Summary
Economic forecasting is a common practice in economics. This is often done either using univariate time series or multivariate economic models. Macroeconometric models are largely guided by economic theory that covers major economic sectors, activities and policies in an economy. They are formulated in a theoretically consistent manner, satisfying economic identities for use in both forecasts and policy analysis. They are data intensive and time consuming. Developing large macroeconomic models could be challenging so, in such cases, forecasting could be done using univariate time series models
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