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Nowcasting Malagasy real GDP using energy data: a MIDAS approach

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  • Ramaharo, Franck M.

Abstract

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.

Suggested Citation

  • Ramaharo, Franck M., 2025. "Nowcasting Malagasy real GDP using energy data: a MIDAS approach," MPRA Paper 126629, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:126629
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    References listed on IDEAS

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    1. Ramaharo, Franck Maminirina & Rasolofomanana, Gerzhino H, 2023. "Nowcasting Madagascar's real GDP using machine learning algorithms," AfricArxiv vpuac, Center for Open Science.
    2. repec:osf:africa:vpuac_v1 is not listed on IDEAS
    3. He, Yongda & Lin, Boqiang, 2018. "Forecasting China's total energy demand and its structure using ADL-MIDAS model," Energy, Elsevier, vol. 151(C), pages 420-429.
    4. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    5. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    6. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas, 2023. "Nowcasting Economic Activity Using Electricity Market Data: The Case of Lithuania," Economies, MDPI, vol. 11(5), pages 1-21, May.
    7. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
    8. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2005. "There is a risk-return trade-off after all," Journal of Financial Economics, Elsevier, vol. 76(3), pages 509-548, June.
    9. Lu, Fei & Ma, Feng & Hu, Shiyang, 2024. "Does energy consumption play a key role? Re-evaluating the energy consumption-economic growth nexus from GDP growth rates forecasting," Energy Economics, Elsevier, vol. 129(C).
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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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