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Solving Heterogeneous Agent Models with Nonconvex Optimization Problems: Linearization and Beyond
1 : Institute for Advanced Studies - Vienna
This paper presents a solution method for heterogeneous agent models where agents solve nonconvex optimization problems. It builds on the linearization approach with state reduction of Reiter (2010). State reduction is used as a basis for global nonlinear solutions in medium-dimensional state space. The method is applied to a model of heterogenous households with indivisible labor supply, and to a model of heterogenous firms with time-varying uncertainty.
