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

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 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.

Keywords:

• Structural VAR
• structural MGARCH
• independent component analysis
• kernel maximum likelihood
• global crude oil market

JEL Classification:

• C14
• C32
• Q43

Disclaimer

As a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.