Abstract:
Rice import forecasts are helping to farmers cope with the decline in paddy production.
Also, forecast the amount of rice imported by consumers, as this indirectly indicates the
amount of local production. The main objective of this study is to develop a time series
model to forecast rice imports in Sri Lanka. Autoregressive moving average (ARMA)
and Exponential generalized autoregressive conditional heteroscedasticity (EGARCH)
models were applied to monthly data collected from Sri Lanka Customs for the period
January 2001 to July 2021 and validated the model using data from August 2021 to July
2022. Mean absolute error (MAE) and Mean squared error (MSE) were employed to
examine the accuracy of forecasting. Based on the results of this study, the ARMA (1,0)
and EGARCH (1,1) models were identified as possible models for rice import
forecasting. However, ARMA (1,0) model is not suitable for forecasting rice imports
due to the presence of heteroscedasticity. Therefore, the EGARCH (1,1) model was
selected as the best model for forecasting. In addition, the rice import forecast for the
next 6 months shows that the volume of rice imports will decrease. The MAE and MSE
for the fitted model are 2.447 and 9.082 respectively. This model needs to be updated
time to time to account for constantly changing data for future forecasts.
