Nowcasting Malagasy real GDP using energy data: a MIDAS approach

In this paper, we investigate the predictive power of petroleum consumption for Malagasy real GDP using the Mixed Data Sampling (MIDAS) framework over the period 2007-2024. While GDP data are available at a quarterly frequency, petroleum consumption is observed monthly and disaggregated by sectoral use and product type. We use this high-frequency disaggregated data to identify which components deliver the strongest nowcasting performance. Our results show that, at the sectoral level, transportation, aviation and bunkers consistently deliver the most accurate GDP nowcasts over the sample period. The best-performing product-level specifications correspond precisely to the fuels predominantly used in these sectors, namely, gas oil, super-unleaded petrol, aviation gasoline, and jet fuel. The aggregate measure of total petroleum consumption also yields competitive forecasting accuracy across specifications. This supports its use as a broad high-frequency indicator of economic activity. Our findings suggest that forecasters of Madagascar’s GDP can significantly improve predictive accuracy by using appropriately disaggregated energy data, particularly from sectoral categories linked to mobility and trade.