Causal inference with (partially) independent shocks and structural signals on the global crude oil market
(2023) , 48 pages
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- Abstract
- Independent component analysis has recently become a promising data-based approach to detect structural relations in multivariate dynamic systems in cases when apriori knowledge about causal patterns are scant. This paper suggests a kernel-based ML estimation that is largely agnostic with regard to the distributional features of the structural origins of data variation and enables causal analysis under the assumption of having only a subset of independent shocks. In an empirical application to the global oil market model of Kilian (2009) we illustrate the benefits of allowing for an unmodelled higher-order dependence among the oil supply and speculative oil demand shocks.
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Citations
Hafner, C., Herwartz, H., & Wang, S. (2023). Causal inference with (partially) independent shocks and structural signals on the global crude oil market (LIDAM Discussion Paper ISBA 2023/04). https://hdl.handle.net/2078.5/102844