Efficient Solution and Computation of Models with Occasionally Binding Constraints

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