Linear non-Gaussian causal discovery from a composite set of major US macroeconomic factors

In this paper, we develop an effective approach to model linear non-Gaussian causal relationships among a composite set of major US macroeconomic factors. The proposed approach first models the linear relationships of the factors using the Vector Autoregression (VAR) model, then the casual relationships are discovered using the linear non-Gaussian Structural Equation Modeling (SEM) method. One advantage of our hybrid approach is that the contemporaneous causal order of macroeconomic variables which is important for VAR practitioners is obtained naturally as a result of the computation. Applying our approach to 11 major US macroeconomic factors reveals that the federal funds rate has the dominating power in the set. This outcome purely based on the underlying data without any prior knowledge is in line with previous studies using other empirical approaches where prior knowledge is often essential. We also provide a global picture depicting the interaction among all the macroeconomic factors of concern, which are often approached individually or in small grouping in the economic research literature in the past and not studied in a unified view as in our approach.

► Get the contemporaneous causal order of macroeconomic variables.
► Reveal that the federal funds rate has the dominating power in a composite set of major US macroeconomic factors.
► Show a path of how macroeconomic factors affect the stock market.
► Provide a global picture depicting the interaction among all the macroeconomic factors of concern.