Statistical Identification of Independent Shocks with Kernel-based Maximum Likelihood Estimation and an Application to the Global Crude Oil Market

Christian M. Hafner, Helmut Herwartz and Shu Wang
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Christian M. Hafner: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2026005, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: 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 article 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, highlighting its ability to capture unmodeled higher-order dependence between oil supply and speculative oil demand shocks.

Keywords: Global crude oil market; Independent component analysis; Kernel maximum likelihood; Structural MGARCH; Structural VAR (search for similar items in EconPapers)
Pages: 16
Date: 2026-02-09
Note: In: Journal of Business & Economic Statistics, 2025, vol. 43(2), p. 423-438
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2026005

DOI: 10.1080/07350015.2024.2388657

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