Recent advances in computing power and the potential to make more realistic assumptions due to increased flexibility have led to the increased prevalence of simulation models in economics. While models of this class, and particularly agent-based models, are able to replicate a number of empirically-observed stylised facts not easily recovered by more traditional alternatives, such models remain notoriously difficult to estimate due to their lack of tractable likelihood functions. While the estimation literature continues to grow, existing attempts have approached the problem primarily from a frequentist perspective, with the Bayesian estimation literature remaining comparatively less developed. For this reason, we introduce a widely-applicable Bayesian estimation protocol that makes use of deep neural networks to construct an approximation to the likelihood, which we then benchmark against a prominent alternative from the existing literature. Overall, we find that our proposed methodology consistently results in more accurate estimates in a variety of settings, including the estimation of financial heterogeneous agent models and the identification of changes in dynamics occurring in models incorporating structural breaks.

计算能力的最新进展以及由于灵活性的提高而可能做出更现实的假设的可能性导致了仿真模型在经济学中的盛行。尽管此类模型（尤其是基于主体的模型）能够复制许多经验观察到的风格化事实，而这些事实是无法通过更传统的替代方法轻易恢复的，但是由于缺乏可预测的似然函数，此类模型仍然难以估计。尽管估计文献继续增长，但是现有尝试主要是从常识角度解决了这个问题，而贝叶斯估计文献的发展还相对较差。为此原因，我们介绍了一种广泛应用的贝叶斯估计协议，该协议利用深度神经网络来构造可能性的近似值，然后我们将其与现有文献中的一个突出选择进行比较。总体而言，我们发现，我们提出的方法在各种情况下都能持续产生更准确的估计，包括金融异质代理模型的估计以及对包含结构性断裂的模型中发生的动力学变化的识别。