Ron Gallant, Raffaella Giacomini, and Giuseppe Ragusa, “Bayesian Estimation of State Space Models Using Moment Conditions.” Journal of Econometrics, 201(2):198-211


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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., timevarying 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.

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I teach economics at the University of Pisa (Department of Economics and Management).


Department of Economics and Management
University of Pisa
Via Cosimo Ridolfi 10
56124 Pisa (PI)


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