Forecasting Disaggregated Producer Prices: A Fusion of Machine Learning and Econometric Techniques

This paper proposes a novel framework to the forecast of disaggregated producer prices using both machine learning techniques and traditional econometric models. Due to the complexity and diversity of pricing dynamics within the euro area, no single model consistently outperforms others across all sectors. This highlights the necessity for a tailored approach that leverages the strengths of various forecasting methods to effectively capture the unique characteristics of each sector. Our forecasting exercise has highlighted diverse pricing strategies linked to commodity prices, autoregressive behavior, or a mixture of both, with pipeline pressures being especially pertinent to final goods. Employing a mixture of a wide range of models has proven to be a successful strategy in managing the varied pricing behavior at the sectoral level. Notably, tree-based methods, like Random Forests or XGBoost, have shown significant efficacy in forecasting short-term PPI inflation across a number of sectors, especially when accounting for pipeline pressures. Moreover, newly proposed Hybrid ARMAX models proved to be a suitable alternative for sectors tightly linked to commodity prices.