I show that the Parameterized Expectations Algorithm (PEA) can be naturally generalized via the bias-corrected Monte Carlo (bc-MC) operator, initially proposed to solve economic models using neural networks. When combined with a parameterized …
Introduction This blog post is about my work on a generalization of the Parameterized Expectations Algorithm, available here.
The Parameterized Expectations Algorithm (PEA) is a classic computational approach to numerically “solve” economic models with rational expectations, i.e. finding an approximate solution. Usually, to solve economic models of this type, one has to find a policy function (e.g. how much to consume today, given a current level of capital) that satisfies a functional equation that holds in expectation (e.
Introduction In this blog post, I am going to present my recent work on the bias-corrected Monte Carlo operator, or more compactly “bc-MC operator”, which was recently published here in the JEDC. In this paper, I propose a new methodology to combine Monte Carlo and neural networks to solve large scale economic models. In this blog post, my goal is to give an intuitive description of this method.
Theory Structure of economic models In many cases, solving an economic model involves finding a policy function $g(.