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<p>Projecting China’s producer price index (PPI) for production materials yields early signals of inflationary pressures and cost dynamics influencing both national economic stability and international supply networks. Reliable PPI forecasts equip policymakers, market participants, and firms with the information needed to refine monetary policy, pricing decisions, and resource allocation. This study proposes an innovative forecasting architecture based on Gaussian process regression (GPR), whose hyperparameters are estimated via a Bayesian inference procedure, enabling the model to adapt in real time to latent market fluctuations and previously unobserved structural shifts. By integrating these evolving characteristics, our approach more accurately captures changes in China’s PPI trajectory. The empirical analysis relies on a monthly dataset spanning October 1996 to February 2025, covering multiple waves of regulatory reform, industrial evolution, and macroeconomic transformation. Validation is performed over an out-of-sample period from June 2019 through February 2025, producing a relative root mean square error of 0.1120%, a root mean square error of 0.1131, a mean absolute error of 0.0832, and a correlation coefficient of 0.99984. To the best of our knowledge, this represents the first application of a Bayesian-inference-parameterized GPR model to forecast China’s PPI for production materials. Beyond advancing the theoretical discourse on machine-learning-based price prediction, the methodology provides a flexible analytical framework applicable to analogous macroeconomic time-series forecasting challenges.</p>

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Forecasting China’s producer price index for production materials via Gaussian process regression within a Bayesian inference framework

  • 期刊论文

  • 发表日期：

    2025年11月6日

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  • Bingzi Jin,
  • Xiaojie Xu

摘要

Projecting China’s producer price index (PPI) for production materials yields early signals of inflationary pressures and cost dynamics influencing both national economic stability and international supply networks. Reliable PPI forecasts equip policymakers, market participants, and firms with the information needed to refine monetary policy, pricing decisions, and resource allocation. This study proposes an innovative forecasting architecture based on Gaussian process regression (GPR), whose hyperparameters are estimated via a Bayesian inference procedure, enabling the model to adapt in real time to latent market fluctuations and previously unobserved structural shifts. By integrating these evolving characteristics, our approach more accurately captures changes in China’s PPI trajectory. The empirical analysis relies on a monthly dataset spanning October 1996 to February 2025, covering multiple waves of regulatory reform, industrial evolution, and macroeconomic transformation. Validation is performed over an out-of-sample period from June 2019 through February 2025, producing a relative root mean square error of 0.1120%, a root mean square error of 0.1131, a mean absolute error of 0.0832, and a correlation coefficient of 0.99984. To the best of our knowledge, this represents the first application of a Bayesian-inference-parameterized GPR model to forecast China’s PPI for production materials. Beyond advancing the theoretical discourse on machine-learning-based price prediction, the methodology provides a flexible analytical framework applicable to analogous macroeconomic time-series forecasting challenges.