论文标题
基于快速模拟的贝叶斯估算异质和代表性代理模型，使用标准化流神经网络
Fast Simulation-Based Bayesian Estimation of Heterogeneous and Representative Agent Models using Normalizing Flow Neural Networks
论文作者
论文摘要
本文提出了一种基于模拟的深度学习贝叶斯程序，以估算宏观经济模型。即使可能无法处理可能性功能，这种方法也能够导致后代。由于贝叶斯估计不需要可能性，因此也不需要过滤。这允许贝叶斯估计具有超过800个潜在状态的HANK模型，并估计了用不产生可能性的方法来求解的代表代理模型 - 例如，投影和价值函数迭代方法。我通过估计通过REITER方法求解的10个参数Hank模型来证明该方法的有效性，该方法通过REITER方法求解，该方法每次步骤生成812个协变量，其中810个是潜在变量，表明该方法可以处理大型潜在空间而无需降低模型。我还使用通过价值函数迭代求解的11参数模型估算了算法，该算法无法用大都市危机甚至常规的最大似然估计器来估计。此外，与大都市杂货店相比，使用基于模拟的推断，我还显示了2007年SMETS-wouter估计的后质质量更高，更快。这种方法有助于解决大都市杂货店的计算费用，并允许解决方案方法，而解决方案方法无法估算可估算的可能性。
This paper proposes a simulation-based deep learning Bayesian procedure for the estimation of macroeconomic models. This approach is able to derive posteriors even when the likelihood function is not tractable. Because the likelihood is not needed for Bayesian estimation, filtering is also not needed. This allows Bayesian estimation of HANK models with upwards of 800 latent states as well as estimation of representative agent models that are solved with methods that don't yield a likelihood--for example, projection and value function iteration approaches. I demonstrate the validity of the approach by estimating a 10 parameter HANK model solved via the Reiter method that generates 812 covariates per time step, where 810 are latent variables, showing this can handle a large latent space without model reduction. I also estimate the algorithm with an 11-parameter model solved via value function iteration, which cannot be estimated with Metropolis-Hastings or even conventional maximum likelihood estimators. In addition, I show the posteriors estimated on Smets-Wouters 2007 are higher quality and faster using simulation-based inference compared to Metropolis-Hastings. This approach helps address the computational expense of Metropolis-Hastings and allows solution methods which don't yield a tractable likelihood to be estimated.
