Statistical identification of independent shocks with kernel-based maximum likelihood estimation and an application to the global crude oil market

Independent component analysis has emerged as a promising approach for revealing structural relationships in multivariate dynamic systems, particularly in scenarios with limited knowledge of causal patterns. This paper introduces a robust kernel-based maximum likelihood (KML) estimation method that accommodates the distributional characteristics of the structural sources of data variation. Our Monte Carlo study demonstrates the superior performance of the KML estimator compared to existing approaches for independence-based identification. Moreover, the proposed method enables partial identification and dimension reduction even in the presence of dependent shocks. We illustrate the benefits of our approach by applying it to the global oil market model of Kilian (2009), highlighting its ability to capture unmodeled higher-order dependence between oil supply and speculative oil demand shocks.

独立成分分析（Independent Component Analysis, ICA）已成为揭示多元动态系统内部结构关系的极具前景的方法，尤其适用于因果模式认知有限的场景。本文提出一种稳健的基于核极大似然（KML）估计方法，可适配数据变异结构性源的分布特性。我们的蒙特卡洛（Monte Carlo）研究表明，相较于现有基于独立性辨识的方法，KML估计器的性能更为优异。此外，即便存在相依冲击，所提方法仍可实现部分辨识与维度约简。我们通过将该方法应用于基利安（Kilian, 2009）的全球石油市场模型，展示了其应用价值，并凸显其能够捕捉石油供给与投机性石油需求冲击间未被建模的高阶相依关系的能力。