Estimation and Inference for Latent Dual Networks Using High-Dimensional IV Screening

Arturas Juodis; George Kapetanios; Vasilis Sarafidis

Estimation and Inference for Latent Dual Networks Using High-Dimensional IV Screening

Econometrics 2026-07-15 v1

Authors: Arturas Juodis , George Kapetanios , Vasilis Sarafidis

Abstract

We develop a novel methodology for estimation and inference in high-dimensional panel network models with latent dual structures. The framework allows outcomes to be affected simultaneously by positive and negative interaction channels, accommodating settings in which some interactions reinforce outcomes while others generate competition and displacement effects. The proposed method identifies and estimates the network directly from the structural model using observed data without the need to pre-specify the network. Network recovery is achieved through a sequential instrumental-variable screening procedure. We establish exact support recovery and oracle-equivalent post-selection inference. An application to U.S. corporate leverage data reveals the coexistence of reinforcing and displacement interactions in firms' financial decisions.

Cite

@article{arxiv.2607.13862,
  title  = {Estimation and Inference for Latent Dual Networks Using High-Dimensional IV Screening},
  author = {Arturas Juodis and George Kapetanios and Vasilis Sarafidis},
  journal= {arXiv preprint arXiv:2607.13862},
  year   = {2026}
}