Ανάρτηση Ερευνητικού Δοκιμίου no 18/25

Ερευνητικό Δοκίμιο no 18/25 με τίτλο "Long Memory from Cheeger Bottlenecks and Long Cycles in Network Dynamics"

του Στέλιου Αρβανίτη

Περίληψη

This note develops a spectral–topological foundation for long memory in network autoregressions. We consider AR(1) dynamics driven by the node Laplacian of a se quence of weighted graphs with weakly dependent innovations. Long memory emerges when the near-zero spectrum of the Laplacian is thickened by either of two structural mechanisms: (i) geometric bottlenecks, which trap flows across network cuts; and (ii) topological long cycles with regularly varying distributions, which yield near harmonic modes. These imply long-memory lower bounds for linear observables and, along with harmonic components, they allow coexistence of random-walk and station ary long-memory behaviors. The framework and could be useful to the design of new applications of network models in economics.

O Στέλιος Αρβανίτης είναι Καθηγητής του τμήματος Οικονομικής Επιστήμης του Οικονομικού Πανεπιστημίου Αθηνών.