This study examines the possibility of applying the novel likelihood-free Bayesian inference called BayesFlow proposed by Radev et al. (2020) for the estimation of agent-based models (ABMs). BayesFlow is a fully likelihood-free approach, which directly approximates a posterior rather than a likelihood function by learning an invertible probabilistic mapping between parameters and standard Gaussian variables, conditional on simulation data from the ABM to be estimated. BayesFlow certainly achieved superior accuracy to the benchmark method of Kernel Density Estimation-MCMC of Grazzini et al. (2017) and the more sophisticated method of Mixture Density Network-MCMC of Platt (2019), in the validation tests of recovering the ground-truth values of parameters from the simulated datasets of a standard New Keynesian ABM (NK-ABM). Furthermore, the truly empirical estimation of NK-ABM with the real data of the US economy successfully showed the desirable pattern of posterior contraction along with the increase in observation periods. This deep neural network-based method holds general applicability without any critical dependence on pre-selected design and high computational efficiency. These features are desirable when scaling the method to practical-sized ABMs, which typically have high-dimensional parameters and observation variables.

这项研究探讨了应用Radev等人提出的称为BayesFlow的新颖无可能性贝叶斯推理的可能性。（2020年）的估计为基础的基于代理的模型（ABMs）。BayesFlow是一种完全无可能性的方法，该方法通过学习参数和标准高斯变量之间的可逆概率映射来直接近似后验函数，而不是似然函数，这取决于要估计的ABM的模拟数据。BayesFlow肯定比Grazzini等人的核密度估计-MCMC基准方法获得了更高的精度。（2017）和Platt（2019）的混合密度网络-MCMC的更复杂方法，是从标准新凯恩斯ABM（NK-ABM）的模拟数据集中恢复参数的真实值的验证测试。此外，用美国经济的真实数据对NK-ABM进行的真实经验估计成功地显示了后收缩的理想模式以及观察期的增加。这种基于深度神经网络的方法具有普遍的适用性，而对预选设计和高计算效率没有任何关键的依赖。当将方法缩放到实际大小的ABM（通常具有高维参数和观察变量）时，这些功能是理想的。