2026-10-10T04:47:24Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33968739 2026-09-22T20:03:57Z category_13 category_21 category_811 category_64 portal_87 item_type_3 month_year_09_2026

Estimating Unobserved Cross-Sectional Network Links with Large-T Panel Data Peter H. Egger (7732061) Jiaqing Zhu (9327641) Genetics Biotechnology Mathematical Sciences not elsewhere classified Cancer Cross-sectional interdependence Global systemically important banks Network econometric models Panel data C23 D85 G21 <p>We propose a simple three-step method to estimate the cross-sectional interdependence in a customary network econometric model with an unobserved network and a large T. First, we estimate the reduced-form model to construct instruments for the endogenous peer outcomes. Second, we use two-stage least squares to estimate the direct and contextual effects of the exogenous regressors, as well as the endogenous peer effect. Third, we estimate the network weight matrix. Monte Carlo results for small- to medium-sized samples indicate that the estimators of the parameters of interest have small biases and root mean squared errors. We apply the approach to estimate the network structure of the 100 largest listed depository institutions worldwide using daily data over 2011-2020. This reveals the detailed structure of interbank network links and identifies influential banks in the data, of which some but not all are Global Systemically Important Banks designated by the Financial Stability Board.</p> 2026-09-22T20:03:57Z Dataset Dataset 10.6084/m9.figshare.33968739.v1 https://figshare.com/articles/dataset/Estimating_Unobserved_Cross-Sectional_Network_Links_with_Large-T_Panel_Data/33968739 CC BY 4.0