Bayesian estimation of state space models using moment conditions

A. Ronald Gallant, Raffaella Giacomini, Giuseppe Ragusa

Research output: Contribution to journalArticle

6 Scopus citations

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 2017

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

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