Bayesian Inference

Simulation-Based Estimation of General Structural Network Models

Structural estimation to fit an economic model on a single graph is an open problem in economics and adjacent fields. While there are techniques to estimate structural models on a collection of independent and identically distributed graphs, there is little literature on a general-purpose algorithm to fit a structural model on a single network--at least work the author is aware of. This paper provides an algorithm borrowed from deep learning that is adapted to network. This approach theoretically allows for Bayesian and likelihood estimation of arbitrary structural models. The main condition is that the graph data is in the support of the networks simulated by the structural model. Tests on simulated data as well as an empirical application demonstrate the validity and the accuracy of this approach.