  We initiate the study of statistical inference and A/B testing for
first-price pacing equilibria (FPPE). The FPPE model captures the dynamics
resulting from large-scale first-price auction markets where buyers use
pacing-based budget management. Such markets arise in the context of internet
advertising, where budgets are prevalent.
  We propose a statistical framework for the FPPE model, in which a limit FPPE
with a continuum of items models the long-run steady-state behavior of the
auction platform, and an observable FPPE consisting of a finite number of items
provides the data to estimate primitives of the limit FPPE, such as revenue,
Nash social welfare (a fair metric of efficiency), and other parameters of
interest. We develop central limit theorems and asymptotically valid confidence
intervals. Furthermore, we establish the asymptotic local minimax optimality of
our estimators. We then show that the theory can be used for conducting
statistically valid A/B testing on auction platforms. Numerical simulations
verify our central limit theorems, and empirical coverage rates for our
confidence intervals agree with our theory.
