Efficient solution and computation of models with occasionally binding constraints

Gregor Boehl

Journal of Economic Dynamics and Control, 2022, vol. 143, issue C

Abstract: Structural estimation of macroeconomic models and new HANK-type models with extremely high dimensionality require fast and robust methods to efficiently deal with occasionally binding constraints (OBCs). This paper proposes a novel algorithm that solves for the perfect foresight path of piecewise-linear dynamic models. In terms of computation speed, the method outperforms its competitors by more than three orders of magnitude. I develop a closed-form solution for the full trajectory given the expected duration of the constraint. This allows to quickly iterate and validate guesses on the expected duration until a perfect-foresight equilibrium is found. A toolbox, featuring an efficient implementation, a model parser and various econometric tools, is provided in the Python programming language. Benchmarking results show that for medium-scale models with an occasionally binding interest rate lower bound, more than 150,000 periods can be simulated per second. Even simulating large HANK-type models with almost 1000 endogenous variables requires only 0.2 milliseconds per period.

Keywords: Occasionally binding constraints; Effective lower bound; Computational methods (search for similar items in EconPapers)
JEL-codes: C63 E32 E58 E63 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Efficient Solution and Computation of Models With Occasionally Binding Constraints (2021)
Working Paper: Efficient solution and computation of models with occasionally binding constraints (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:143:y:2022:i:c:s0165188922002275

DOI: 10.1016/j.jedc.2022.104523

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