Estimating DSGE Models: Recent Advances and Future Challenges
综述了动态随机一般均衡（DSGE）模型的估计方法，介绍了状态空间表示、矩和似然函数评估，以及粒子滤波、近似贝叶斯计算、哈密顿蒙特卡洛、变分推断和机器学习等前沿技术，并指出三个未来挑战。
We review the current state of the estimation of dynamic stochastic general equilibrium (DSGE) models. After introducing a general framework for dealing with DSGE models, the state-space representation, we discuss how to evaluate moments or the likelihood function implied by such a structure. We discuss, in varying degrees of detail, recent advances in the field, such as the tempered particle filter, approximated Bayesian computation, Hamiltonian Monte Carlo, variational inference, and machine learning. These methods show much promise but have not been fully explored by the DSGE community yet. We conclude by outlining three future challenges for this line of research.