Ron Gallant, Raffaella Giacomini, and Giuseppe Ragusa, “Bayesian Estimation of State Space Models Using Moment Conditions.” Forthcoming in Journal of Econometrics

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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.


« Existence and Characterization of Conditional Density Projections | Publications List | Sensitivity, Moment Conditions, and the Risk-free Rate in Yogo (2006) »

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Giuseppe Ragusa teaches in the Department of Economics and Business and in the Business School at Luiss University. His research is mostly about econometrics.

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