Bayesian estimation of state space models using moment conditions

Andrew Ronald Gallant, Raffaella Giacomini, Giuseppe Ragusa

Research output: Contribution to journalArticle

2 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)198-211
Number of pages14
JournalJournal of Econometrics
Volume201
Issue number2
DOIs
StatePublished - Dec 1 2017

Fingerprint

Moment conditions
Bayesian estimation
State-space model
Latent variables
Time-varying parameters
Dynamic general equilibrium model
Stochastic volatility
Stochastic dynamics
Partial equilibrium model
Approximation

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Cite this

Gallant, Andrew Ronald ; Giacomini, Raffaella ; Ragusa, Giuseppe. / Bayesian estimation of state space models using moment conditions. In: Journal of Econometrics. 2017 ; Vol. 201, No. 2. pp. 198-211.
@article{006bb24bbdfc4c06af55a0f19bc60247,
title = "Bayesian estimation of state space models using moment conditions",
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.",
author = "Gallant, {Andrew Ronald} and Raffaella Giacomini and Giuseppe Ragusa",
year = "2017",
month = "12",
day = "1",
doi = "10.1016/j.jeconom.2017.08.003",
language = "English (US)",
volume = "201",
pages = "198--211",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "2",

}

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 journalArticle

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 -