  Large scale networks delineating collective dynamics often exhibit cascading
failures across nodes leading to a system-wide collapse. Prominent examples of
such phenomena would include collapse on financial and economic networks.
Intertwined nature of the dynamics of nodes in such network makes it difficult
to disentangle the source and destination of a shock that percolates through
the network, a property known as reflexivity. In this article, a novel
methodology is proposed which combines vector autoregression model with an
unique identification restrictions obtained from the topological structure of
the network to uniquely characterize cascades. In particular, we show that
planarity of the network allows us to statistically estimate a dynamical
process consistent with the observed network and thereby uniquely identify a
path for shock propagation from any chosen epicenter to all other nodes in the
network. We analyze the distress propagation mechanism in closed loops giving
rise to a detailed picture of the effect of feedback loops in transmitting
shocks. We show usefulness and applications of the algorithm in two networks
with dynamics at different time-scales: worldwide GDP growth network and stock
network. In both cases, we observe that the model predicts the impact of the
shocks emanating from the US would be concentrated within the cluster of
developed countries and the developing countries show very muted response,
which is consistent with empirical observations over the past decade.
