“The Estimation of Diffusion Processes with Private Network Information” (Paper)
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 econometric 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 Bayesian players observe their close neighbors. We demonstrate the existence of the equilibrium of the model and characterize the symmetric equilibrium. Based on these theoretical findings, we propose a consistent and tractable two-step estimator for payoff parameters using feasible data from a single large network. We evaluate the finite sample performance using Monte Carlo simulations, and apply our method to the network data from Banerjee et al. (2013).
"The Identification and Estimation of Many-to-Many Matching Games"
This paper studies a market with many-to-many contracts when the number of market participants increases. Many-to-many contracts allow a seller to trade with multiple buyers and a buyer to trade with multiple sellers. We identify payoff parameters through data observed from equilibrium matches in a large many-to-many matching market. Several issues must be addressed in many-to-many matching markets: 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) 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 those in a one-to-one matching framework.