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Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints

S. Boragan Aruoba (), Pablo Cuba-Borda () and Frank Schorfheide ()
Authors registered in the RePEc Author Service: Sergio Villalvazo ()

No 20-13, Working Papers from Federal Reserve Bank of Philadelphia

Abstract: We develop an algorithm to construct approximate decision rules that are piecewise-linear and continuous for DSGE models with an occasionally binding constraint. The functional form of the decision rules allows us to derive a conditionally optimal particle filter (COPF) for the evaluation of the likelihood function that exploits the structure of the solution. We document the accuracy of the likelihood approximation and embed it into a particle Markov chain Monte Carlo algorithm to conduct Bayesian estimation. Compared with a standard bootstrap particle filter, the COPF significantly reduces the persistence of the Markov chain, improves the accuracy of Monte Carlo approximations of posterior moments, and drastically speeds up computations. We use the techniques to estimate a small-scale DSGE model to assess the effects of the government spending portion of the American Recovery and Reinvestment Act in 2009 when interest rates reached the zero lower bound.

Keywords: Bayesian Estimation; Nonlinear Filtering; Nonlinear Solution Methods; Par-ticle MCMC; ZLB (search for similar items in EconPapers)
JEL-codes: C5 E4 E5 (search for similar items in EconPapers)
Pages: 62
Date: 2020-04-06
New Economics Papers: this item is included in nep-dge, nep-ecm, nep-mac and nep-ore
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Related works:
Working Paper: Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints (2020) Downloads
Working Paper: Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints (2020) Downloads
Working Paper: Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints (2020) Downloads
Working Paper: Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints (2020) Downloads
Journal Article: Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedpwp:87720

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DOI: 10.21799/frbp.wp.2020.13

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