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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 198-211 |
| Number of pages | 14 |
| Journal | Journal of Econometrics |
| Volume | 201 |
| Issue number | 2 |
| DOIs | |
| State | Published - Dec 1 2017 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Economics and Econometrics
Cite this
}
Bayesian estimation of state space models using moment conditions. / Gallant, Andrew Ronald; Giacomini, Raffaella; Ragusa, Giuseppe.
In: Journal of Econometrics, Vol. 201, No. 2, 01.12.2017, p. 198-211.Research output: Contribution to journal › Article
TY - JOUR
T1 - Bayesian estimation of state space models using moment conditions
AU - Gallant, Andrew Ronald
AU - Giacomini, Raffaella
AU - Ragusa, Giuseppe
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85030856795&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030856795&partnerID=8YFLogxK
U2 - 10.1016/j.jeconom.2017.08.003
DO - 10.1016/j.jeconom.2017.08.003
M3 - Article
AN - SCOPUS:85030856795
VL - 201
SP - 198
EP - 211
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 2
ER -