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Stochastic simulation aims to compute output performance for complex modelsthat lack analytical tractability. To ensure accurate prediction, the modelneeds to be calibrated and validated against real data. Conventional methodsapproach these tasks by assessing the model-data match via simple hypothesistests or distance minimization in an ad hoc fashion, but they can encounterchallenges arising from non-identifiability and high dimensionality. In thispaper, we investigate a framework to develop calibration schemes that satisfyrigorous frequentist statistical guarantees, via a basic notion that we calleligibility set designed to bypass non-identifiability via a set-basedestimation. We investigate a feature extraction-then-aggregation approach toconstruct these sets that target at multivariate outputs. We demonstrate ourmethodology on several numerical examples, including an application tocalibration of a limit order book market simulator (ABIDES).

Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set