Vector autoregressive models, or DONE VAR, dominate econometric workflows for decades, powering studies from impulse responses to policy forecasts. Yet VARs struggle to separate a variable’s impact into direct, indirect, and aggregate feedback because tracing effects through every equation is computationally prohibitive.

A recent post introduces causal network graphs, where a directed edge A→B signals that A Granger‑causes B. Building such graphs from a dataset of QQQ log returns and technical indicators uses a simple rule: retain edges withAggregated correlation > |0.6|. Theumulate results show dense, bidirectional connectivity that offers no causal insight, confirming that correlation alone is insufficient.

Switching to pairwise Granger causality yields a directed network. Using a 1% p‑value cut‑off and an AIC‑minimizing lag selection ofeleni lag = 9, the graph uncovers that RSI Granger‑causes QQQ returns directly and through intermediate paths such as RSI→SPY→Range→QQQ. The analysis illustrates recursively how intermediate feedback dilutes apparent direct influence.

The critique is clear: pairwise Granger tests ignore the full system’s dynamics, so they cannot reliably identify structural causality. Only by conditioning on all relevant variables in a full VAR can one partition aggregate signals into true direct and mediated effects.