Solving Heterogeneous Agent Models with Dispersed Information in Sequence Space
Working paper 2026
This paper studies how to solve rational expectations models in which households do not directly observe the aggregate objects that matter for their decisions. I consider economies with rational expectations, heterogeneous agents, endogenous equilibrium aggregates, and signals that combine aggregate and idiosyncratic components. The main contribution is a method that embeds signal extraction into a sequence space solution approach. The key factor is aggregate linearity, an assumption that is standard in sequence space methods. I show that this assumption implies a mapping that can be used to apply a Kalman filter for signal extraction, making it possible to solve for transition paths. The method is illustrated in a small open economy model with incomplete markets and endogenous labor supply, where households infer the aggregate state from their own observed wages.