An Improved Auxiliary Particle Filter for Nonlinear Dynamic Equilibrium Models
Yuan Yang and
Lu Wang
No 47, Dynare Working Papers from CEPREMAP
Abstract:
We develop a procedure that efficiently computes likelihood function in nonlinear dynamic stochastic general equilibrium (DSGE) models. The procedure employs linearization to the measurement equation and delivers competitive results as the fully-adapted particle filter. The resulting likelihood approximation has much lower Monte Carlo variance than currently available particle filters, which greatly enhances the likelihood-based inference of DSGE models. We illustrate our procedure in applications to Bayesian estimation of a new Keynesian macroeconomic model.
Keywords: DSGE model; auxiliary particle filter; Bayesian estimation (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 C63 (search for similar items in EconPapers)
Pages: 8 pages
Date: 2015-11
New Economics Papers: this item is included in nep-dge, nep-ets and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:cpm:dynare:047
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