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

No 27991, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C5 E4 E5 (search for similar items in EconPapers)
Date: 2020-10
New Economics Papers: this item is included in nep-cmp, nep-dge, nep-ets, nep-mac and nep-ore
Note: EFG ME
References: Add references at CitEc
Citations: View citations in EconPapers (8)

Published as S. Borağan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2021. "Piecewise-linear approximations and filtering for DSGE models with occasionally-binding constraints," Review of Economic Dynamics, vol 41, pages 96-120.

Downloads: (external link)
http://www.nber.org/papers/w27991.pdf (application/pdf)

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)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:27991

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w27991

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().