Journal of Econometrics

J. Econometrics Vol. 201 (2) pp. 198--211

Bayesian Estimation of State Space Models Using Moment Conditions

^a A. Ronald Gallant
^b Raffaella Giacomini
^c Giuseppe Ragusa

^aPenn State University
^bUniversity College London
^cLuiss University

Available online 1 September 2017.

Abstract

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.

JEL Classification: C32; C36; E27

Keyword(s): State Space Models, Bayesian Estimation, Moment Equations, Structural Models, DSGE Models, Particle Filter