Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models

Thomas Flury and Neil Shephard ()

OFRC Working Papers Series from Oxford Financial Research Centre

Abstract: Suppose we wish to carry out likelihood based inference but we solely have an unbiased simulation based estimator of the likelihood. We note that unbiasedness is enough when the estimated likelihood is used inside a Metropolis-Hastings algorithm. This result has recently been intro- duced in statistics literature by Andrieu, Doucet, and Holenstein (2007) and is perhaps surprising given the celebrated results on maximum simulated likelihood estimation. Bayesian inference based on simulated likelihood can be widely applied in microeconomics, macroeconomics and financial econometrics. One way of generating unbiased estimates of the likelihood is by the use of a particle filter. We illustrate these methods on four problems in econometrics, producing rather generic methods. Taken together, these methods imply that if we can simulate from an economic model we can carry out likelihood based inference using its simulations.

Keywords: Dynamic stochastic general equilibrium models; inference; likelihood; MCMC; Metropolis-Hastings; particle filter; state space models; stochastic volatility (search for similar items in EconPapers)
JEL-codes: C11 C13 C15 C32 E32 (search for similar items in EconPapers)
Pages: 28
Date: 2008
New Economics Papers: this item is included in nep-dge, nep-ecm, nep-ets and nep-mac
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Citations: View citations in EconPapers (19)

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Related works:
Journal Article: BAYESIAN INFERENCE BASED ONLY ON SIMULATED LIKELIHOOD: PARTICLE FILTER ANALYSIS OF DYNAMIC ECONOMIC MODELS (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:sbs:wpsefe:2008fe32

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