Identification and Inference in Nonlinear Dynamic Network Models
We study identification and inference in nonlinear dynamic systems defined on unknown interaction networks. The system evolves through an unobserved dependence matrix governing cross-sectional shock propagation via a nonlinear operator. We show that the network structure is not generically identified, and that identification requires sufficient spectral heterogeneity. In particular, identification arises when the network induces non-exchangeable covariance patterns through heterogeneous amplification of eigenmodes. When the spectrum is concentrated, dependence becomes observationally equivalent to common shocks or scalar heterogeneity, leading to non-identification. We provide necessary a...
| Authors | Diego Vallarino |
|---|---|
| Primary category | stat.ML |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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