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.
Authors
![Raffaella Giacomini](/sites/default/files/styles/square_desktop/public/2022-05/Raffaella_Giacomini.jpg?itok=C7umU0x0)
Research Associate University College London
Raffaella is a Professor in the Department of Economics at UCL and a Senior Economist at the Federal Reserve Bank of Chicago.
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Duke University
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Giuseppe Ragusa
Journal article details
- DOI
- 10.1016/j.jeconom.2017.08.003
- Publisher
- Elsevier
- JEL
- C32, C36, E27
- Issue
- December 2017, pages 198-211
Suggested citation
R, Gallant and R, Giacomini and G, Ragusa. (2017). 'Bayesian estimation of state space models using moment conditions' (2017), pp.198–211.
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