What question did this study set out to answer?

The research aims to compare econometric and machine learning approaches for predicting food inflation in Bangladesh.

January 20, 2026Open Access

Benchmarking econometric, decomposable additive, and neural network methods for food inflation prediction featuring policy insights

Key Points

The research aims to compare econometric and machine learning approaches for predicting food inflation in Bangladesh.
Analyzed food inflation data from July 2010 to March 2025.
Employed SARIMAX, TDANN, Prophet, and LSTM models for predictions.
Focused on the impact of climate variables and the Energy Price Index.
ANN model showed the lowest error metrics among tested methods.
Research supports nonlinear ML models as superior for predicting food inflation.
Findings provide actionable policy insights for managing energy markets and agricultural inputs.

Abstract

With a longstanding struggle to achieve food self-sufficiency, Bangladesh is facing a severe food security crisis, as highlighted by its placement on the World Bank’s “Red List” for food inflation for two consecutive years. Both the World Bank and the Bangladesh Bureau of Statistics (BBS) confirm that food inflation has exceeded 10% over the past year. The crisis of rising food costs, attributed to a combination of global, local, and structural issues, is exacerbating socioeconomic hardships for low-income households. It necessitated an in-depth investigation into the external factors influencing food inflation at the domestic level, acknowledging that unprecedented inflation is one of the biggest hindrances to economic growth and development. Existing research on food inflation is extensive, yet the majority of these studies employ statistical/econometric models and fail to produce comparative analyses. Seeking to address this gap, the author analyzed the combined impact of climate variables and the Energy Price Index on the Food Price Index, employing machine learning models with monthly data of Bangladesh from July 2010 to March 2025. The study proposed four distinct time series models: SARIMAX, TDANN, Prophet, and LSTM. Out of all the tested models, with the lowest error metrics (RMSE and MAE), ANN 6 stood as the best, supporting the hypothesis of this research that nonlinear ML models are better at predicting food inflation than the traditional models. Based on the key findings from Explainable AI (SHAP) and decomposed Prophet component analysis, the study presents solid, instantaneous focus-based policy implications for the agricultural input and energy markets, addressing problems from both sides to better manage uncertainty during weather shocks and energy price volatility. The study can serve as a reliable reference for researchers and policymakers to inform strategies for mitigating the ongoing food security crisis in Bangladesh.

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