State-observation Sampling And The Econometrics Of Learning Models
In nonlinear state-space models, sequential learning about the hidden state can proceed by particle filtering when the density of the observation conditional on the state is available analytically (e.g. Gordon et al. 1993). This condition need not hold in complex environments, such as the incomplete-information equilibrium models considered in financial economics. In this paper, the authors make two contributions to the learning literature. First, they introduce a new filtering method, the State-Observation Sampling (SOS) filter, for general state-space models with intractable observation densities. Second, they develop an indirect inference-based estimator for a large class of incomplete-information economies.