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

2026-07-15

AI Summary

This study addresses the identification and inference of latent dual interaction structures—characterized by simultaneous positive reinforcement and negative exclusion effects—in high-dimensional panel network models. Without requiring prior knowledge of the network topology, the authors propose an estimation framework based on sequential instrumental variable screening. This approach achieves exact support recovery for the latent dual interaction network for the first time and attains oracle-equivalent statistical inference performance after variable selection. Theoretically, the method integrates high-dimensional instrumental variable screening, structural model estimation, and post-selection inference. Empirically, it is applied to corporate leverage data, revealing the coexistence of reinforcement and exclusion effects in firms’ financial decisions and thereby demonstrating the method’s validity and robustness.

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.

Problem

• latent dual networks
• high-dimensional
• instrumental variable
• network inference
• panel data

Innovation

• latent dual networks
• high-dimensional IV screening
• network recovery
• post-selection inference
• panel network models