Single-Network Finite-Sample Inference in Strategic Network Formation Models

Abstract

We develop a finite-sample valid inference procedure for strategic network formation models in which linking decisions depend on endogenous network statistics (say, the number of common friends). Only a single network is required to be observed, and we restrict neither its density, nor the dependence structure induced by strategic interaction, nor the equilibrium selection mechanism. We exploit a bounding-by-c technique to construct a set of sandwich inequalities that are valid realization by realization, with the middle term involving only the i.i.d. pairwise error. We then average the sandwich inequalities over cells of exogenous covariates, and obtain identifying restrictions under a nonstandard pathwise limit formulation. For inference, we construct test statistics whose finite-sample uncertainty can be controlled by statistics of the exogenous covariates and errors alone, whose conditional distributions are exactly simulable in both semiparametric and parametric settings. Our proposed inference procedure is also computationally tractable, with no need to solve, simulate, or enumerate equilibrium network structures. In simulations, our procedure easily scales to networks of size 10,000, and yields confidence sets that certifies the sign of the strategic coefficient. In two empirical applications (with network size about 300~9500), we find statistical evidence for positive link interdependence at 95% confidence level.