Enhancing Nigerian Oil Price Forecasting: A Comprehensive Analysis of Model Averaging Techniques

Abstract

Numerous fields of endeavour have benefited greatly from statistical forecasting, which has aided decision-making by planners and policy makers. In this study, Bayesian Model Averaging (BMA) and Dynamic Model Averaging (DMA) are employed to forecast oil prices in Nigeria. It aimed at predicting the oil prices in Nigeria. Essentially, there are lot of model uncertainties in empirical growth researches. The predictive performance value considering the Mean Squared Forecast Error (MSFE) for BMA and DMA were 920.23 & 540.40 respectively. The DMA predicted the model better than the BMA. High levels of model uncertainties were indeed accounted for, in conformity with the theoretical knowledge.