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

Frank Schorfheide, S. Boragan Aruoba, Pablo Cuba-Borda, Kenji Hilga-Flores and Sergio Villalvazo

No 15388, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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; Effective lower bound on nominal interest rates; Nonlinear filtering; Nonlinear solution methods; Particle mcmc (search for similar items in EconPapers)
JEL-codes: C5 E4 E5 (search for similar items in EconPapers)
Date: 2020-10
New Economics Papers: this item is included in nep-dge and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Related works:
Journal Article: Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints (2021)
Working Paper: Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints (2020)
Working Paper: Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints (2020)
Working Paper: Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints (2020)
Working Paper: Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints (2020)
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