Efficient Solution and Computation of Models with Occasionally Binding Constraints

19 Pages Posted: 19 Jul 2022

Date Written: July 6, 2022

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

JEL Classification: E63, C63, E58, E32, C62

Suggested Citation

Boehl, Gregor, Efficient Solution and Computation of Models with Occasionally Binding Constraints (July 6, 2022). Available at SSRN: https://ssrn.com/abstract=4155283 or http://dx.doi.org/10.2139/ssrn.4155283

Gregor Boehl (Contact Author)

University of Bonn ( email )

Adenauerallee 24-42
Bonn, D-53113
Germany

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