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
Additional contact information
Kenji Higa-Flores: University of Maryland

Review of Economic Dynamics, 2021, vol. 41, 96-120

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. (Copyright: Elsevier)

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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
https://dx.doi.org/10.1016/j.red.2020.12.003
Access to full texts is restricted to ScienceDirect subscribers and institutional members. See https://www.sciencedirect.com/ for details.

Related works:
Software Item: Code and data files for "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints" (2021)
Working Paper: Online Appendix to "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)
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:red:issued:20-14

Ordering information: This journal article can be ordered from
https://www.economic ... ription-information/

DOI: 10.1016/j.red.2020.12.003

Access Statistics for this article

Review of Economic Dynamics is currently edited by Loukas Karabarbounis

More articles in Review of Economic Dynamics from Elsevier for the Society for Economic Dynamics Contact information at EDIRC.
Bibliographic data for series maintained by Christian Zimmermann ().