 Tractable Latent State Filtering for Non-Linear DSGE Models Using a Second-Order Approximation and PruningULB Institutional Repository from ULB -- Universite Libre de Bruxelles Abstract: This paper develops a novel approach for estimating latent state variables of Dynamic Stochastic General Equilibrium (DSGE) models that are solved using a second-order accurate approximation. I apply the Kalman filter to a state-space representation of the second-order solution based on the ‘pruning’ scheme of Kim et al. (J Econ Dyn Control 32:3397–3414, 2008). By contrast to particle filters, no stochastic simulations are needed for the deterministic filter here; the present method is thus much faster; in terms of estimation accuracy for latent states it is competitive with the standard particle filter. Use of the pruning scheme distinguishes the filter here from the deterministic Quadratic Kalman filter presented by Ivashchenko (Comput Econ, 43:71–82, 2014). The filter here performs well even in models with big shocks and high curvature. Keywords: DSGE model estimation; Latent state filtering; Pruning; Quadratic Kalman filter; Second-order approximation (search for similar items in EconPapers) Published in: Computational economics (2014) v.45 n° 2,p.239-260 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ulb:ulbeco:2013/250061 Ordering information: This working paper can be ordered from Access Statistics for this paper More papers in ULB Institutional Repository from ULB -- Universite Libre de Bruxelles Contact information at EDIRC. |
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