2014
DOI: 10.17016/feds.2014.047
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OccBin: A Toolkit for Solving Dynamic Models with Occasionally Binding Constraints Easily

Abstract: We describe how to adapt a first-order perturbation approach and apply it in a piecewise fashion to handle occasionally binding constraints in dynamic models. Our examples include a real business cycle model with a constraint on the level of investment and a New Keynesian model subject to the zero lower bound on nominal interest rates. We compare the piecewise linear perturbation solution with a high-quality numerical solution that can be taken to be virtually exact. The piecewise linear perturbation method ca… Show more

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Cited by 137 publications
(246 citation statements)
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References 28 publications
(41 reference statements)
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“…-Our algorithm imposes the ELB in a manner similar to that of and Luca Guerrieri and Matteo Iacoviello (2015). We assume that agents never expect the ELB to bind for more than 15 years.…”
Section: Iic Our Simulation Approachmentioning
confidence: 99%
“…-Our algorithm imposes the ELB in a manner similar to that of and Luca Guerrieri and Matteo Iacoviello (2015). We assume that agents never expect the ELB to bind for more than 15 years.…”
Section: Iic Our Simulation Approachmentioning
confidence: 99%
“…, p l , j and m. After detrending the variables by their balanced growth trends, we use the methods described in Appendix B and more fully developed in Guerrieri and Iacoviello (2013) to solve the model subject to the two occasionally binding constraints given by equations A.13 and A.33.…”
Section: Appendix Appendix a Equilibrium Conditions Of The Full Modelmentioning
confidence: 99%
“…We use the OccBin solution method developed by Guerrieri and Iacoviello (2015) to treat the occasionally binding constraint via a piecewise linear solution. Moreover, we use an algorithm as in Giovannini and Ratto (2017) to obtain smoothed estimates of latent variables as well as the sequence of regimes along the historical sample.…”
Section: Implementation Of the Occasionally Binding Constraintmentioning
confidence: 99%
“…We use the algorithm byGiovannini and Ratto (2017) to obtain estimated latent variables and a corresponding sequence of historical regimes. The algorithm works as follows: It guesses an initial sequence of historical regimes and computes the sequence of state-space matrices following the piecewise linear solution byGuerrieri and Iacoviello (2015). Given the state-space matrices, the Kalman filter is used to estimate the smoothed variables and shocks, which endogenously determine a new sequence of regimes.…”
mentioning
confidence: 99%