Session

DescriptionSerguei Maliar's paper applies deep learning to macroeconomic models. The goal of his paper is to demonstrate that deep learning techniques can be used to analyze rather complex economic models in a simple and general manner. They show how to cast the typical dynamic economic model into a form that is suitable for deep learning analysis, and how to design a version of deep learning algorithm that can construct a numerical solution to the model. Xavier Ragot will solve for optimal Ramsey policies in heterogeneous-agent models with aggregate shocks. They provide a new simple theory based on projection on the space of idiosyncratic histories, to present a finite-dimensional state-space representation. Gregor Boehl will present a solution method and a Bayesian smoother to filter and estimate rational expectations models with occasionally binding constraints quickly and accurately. Ralph Luetticke's paper describes a method for solving heterogeneous agent models with aggregate risk and many idiosyncratic states formulated in discrete time. It extends the method proposed by Reiter (2009) and complements recent work by Ahn et al. (2017) on how to solve such models in continuous time.