Journal Article

Bayesian estimation of state space models using moment conditions


Ron Gallant, Raffaella Giacomini, Giuseppe Ragusa

Published Date

18 December 2017


Journal Article

We consider Bayesian estimation of state space models when the measurement density is not available but estimating equations for the parameters of the measurement density are available from moment conditions. The most common applications are partial equilibrium models involving moment conditions that depend on dynamic latent variables (e.g., time–varying parameters, stochastic volatility) and dynamic general equilibrium models when moment equations from the first order conditions are available but computing an accurate approximation to the measurement density is difficult.