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Bayesian estimation of state space models using moment conditions

Gallant, AR; Giacomini, R; Ragusa, G; (2017) Bayesian estimation of state space models using moment conditions. Journal of Econometrics , 201 (2) pp. 198-211. 10.1016/j.jeconom.2017.08.003. Green open access

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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.

Type: Article
Title: Bayesian estimation of state space models using moment conditions
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.jeconom.2017.08.003
Publisher version: https://doi.org/10.1016/j.jeconom.2017.08.003
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: State Space Models, Bayesian Estimation, Moment Equations, Structural Models, DSGE Models, Particle Filter
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/1522255
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