The Estimation of Diffusion Processes with Private Network Information (Job Market Paper, PDF)
Abstract: Innovations, either products or ideas, often diffuse in the population via social ties. This paper studies the identification and estimation of diffusion processes in social and economic networks. Compared to the classic diffusion literature that assumes a continuous population with a stochastic network structure, we provide a new econometric framework to analyze diffusion processes in fixed networks where strategic players hold private information about their links. Extending Sadler (2020), we propose a model where Bayesian players observe their close neighbors. We demonstrate the existence of the equilibrium of the model, and characterize the unique symmetric equilibrium. Based on these theoretical findings, we propose a consistent and tractable two-step estimator for individual payoffs using feasible data from a single large networks. We evaluate the finite sample performance using Monte Carlo simulations.
Identification and Estimation of Many-to-Many Matching Games
Abstract: This paper studies a market with many-to-many contracts when the number of market participants grows large. Many-to-many contracts allow a seller to trade with multiple buyers and a buyer to trade with multiple sellers. We focus on investigating the identification of payoff parameters. In many-to-many matching markets, several issues have to be addressed: bias would arise since the outcomes are only observed when links are formed between two agents, and the maximum number of relationships an agent can enter into would possibly affect the set of stable outcomes. We show that under certain conditions, the number of firms (workers) that are willing to be matched to a specific worker (firm) grows at a rate regardless of the capacity of both sides. Furthermore, we show a correspondence between the stable matching outcomes in a many-to-many matching framework and that in a one-to-one matching framework.
Work in Progress
The Identification of Product Adoption Games
Abstract: This project studies a model of innovation diffusion where heterogeneous agents have the option of adopting the invention or postponing their decision until enough neighbors adopt. I provide conditions that guarantee the models equilibrium, and demonstrate the identification of payoff parameters when the number of agents in the network increases to infinity. The identification is achieved when the network links, agent types, and the final adoption decisions are observed.