arXiv subjects

David Jacho-Chavez

Publications and source records attributed to David Jacho-Chavez.

3 recordsLinked to original sources

arXiv2026

Regression with Observational Multilayered Network Data

A novel method to estimate social effect coefficients in the popular so-called linear-in-means regression model in the Social Sciences is presented here that utilizes non-experimental multidimensional network data. The procedure can accommodate social interactions that correlate with the error in the model by making use of a different set of network links among the same observations that are exogenous in the traditional sense. In particular, the full observability of a two-layered multiplex network data structure is assumed here to propose a new Generalized 3-Stage Least Squares (G3SLS) estimator that is consistent, asymptotically normally distributed, and also easy to implement using widely-used existing statistical software because of its closed-form definition. The underlying assumptions are general enough to accommodate common problems with observational data such as measurement error, simultaneity, and unobserved heterogeneity. Monte Carlo exercises confirm the good small sample performance of the proposed G3SLS estimator in these scenarios. An empirical application finds positive and significant peer effects in citations among research articles published in top general-interest journals in economics.

econ.EM↗

arXiv2026

Estimating Peer Influence in Multilayer Networks

This paper proposes a model-based empirical method to identify influential individuals in risky behaviors. To determine the most influential individuals, we estimate peer influence using observational cross-sectional data from multiple social connections. Our empirical strategy employs the observed characteristics of distant individuals across multiple social networks as instruments to address the endogeneity arising from homophily. Using Add Health data, we find positive peer effects from friends and classmates on both cigarette smoking and marijuana use. Based on the estimated peer effects, we characterize the influencers in our sample.

econ.EM↗

arXiv2026

Estimating Social Effects with Randomized and Observational Network Data

This paper introduces an innovative approach to identifying and estimating the parameters of interest in the widely recognized linear-in-means regression model under conditions where the initial randomization of peers determines the observed network. We assert that peers who are initially randomized do not produce social effects. However, after randomization, agents can endogenously develop significant connections that potentially generate peer influences. We present a moment condition that compiles local heterogeneous identifying information for all agents within the population. Under the assumption of $\psi$-dependence in the endogenous network space, we propose a Generalized Method of Moments (GMM) estimator, which is proven to be consistent, asymptotically normally distributed, and straightforward to implement using commonly available statistical software due to its closed-form expression. Monte Carlo simulations demonstrate the GMM estimator's strong small-sample performance. An empirical analysis utilizing data from Hong Kong high school students reveals substantial positive spillover effects on math test scores among study partners in our sample, provided that their seatmates were exogenously assigned by their teachers.

econ.EM↗