  Simulation models, in particular agent-based models, are gaining popularity
in economics. The considerable flexibility they offer, as well as their
capacity to reproduce a variety of empirically observed behaviours of complex
systems, give them broad appeal, and the increasing availability of cheap
computing power has made their use feasible. Yet a widespread adoption in
real-world modelling and decision-making scenarios has been hindered by the
difficulty of performing parameter estimation for such models. In general,
simulation models lack a tractable likelihood function, which precludes a
straightforward application of standard statistical inference techniques.
Several recent works have sought to address this problem through the
application of likelihood-free inference techniques, in which parameter
estimates are determined by performing some form of comparison between the
observed data and simulation output. However, these approaches are (a) founded
on restrictive assumptions, and/or (b) typically require many hundreds of
thousands of simulations. These qualities make them unsuitable for large-scale
simulations in economics and can cast doubt on the validity of these inference
methods in such scenarios. In this paper, we investigate the efficacy of two
classes of black-box approximate Bayesian inference methods that have recently
drawn significant attention within the probabilistic machine learning
community: neural posterior estimation and neural density ratio estimation. We
present benchmarking experiments in which we demonstrate that neural network
based black-box methods provide state of the art parameter inference for
economic simulation models, and crucially are compatible with generic
multivariate time-series data. In addition, we suggest appropriate assessment
criteria for future benchmarking of approximate Bayesian inference procedures
for economic simulation models.
