Algorithms for Solving Dynamic Models with Occasionally Binding Constraints
We describe and compare several algorithms for approximating .the solution to a model in which inequality constraints occasionally bind. their performance is evaluated and compared using various parameterizations of the one sector growth model with irreversible investment. we develop parameterized expectation algorithms which, on the basis of speed, accuracy and convenience of implementation, appear to dominate the other algorithms.
Suggested citation: Christiano, Lawrence, and Jonas D.M. Fisher, 1997. “Algorithms for Solving Dynamic Models with Occasionally Binding Constraints,” Federal Reserve Bank of Cleveland, Working Paper no. 97-11.