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

S. Boragan Aruoba, Pablo Cuba-Borda, Kenji Higa-Flores, Frank Schorfheide and Sergio Villalvazo
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Kenji Higa-Flores: University of Maryland

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

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 ?lter (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 ?lter, the COPF signi?cantly 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 e?ects 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; E?ective 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)
Pages: 78 pages
Date: 2020-10-09
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Citations: View citations in EconPapers (7)

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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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