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.
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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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