Skip to:
  1. Main navigation
  2. Main content
  3. Footer
Working Paper

Algorithms for Solving Dynamic Models with Occasionally Binding Constraints

Download pdf
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.
10.01.1997ISSN 2573-7953 WP 97-11DOI 10.26509/frbc-wp-199711

Working Papers of the Federal Reserve Bank of Cleveland are preliminary materials circulated to stimulate discussion and critical comment on research in progress. They may not have been subject to the formal editorial review accorded official Federal Reserve Bank of Cleveland publications. The views expressed in this paper are those of the authors and do not represent the views of the Federal Reserve Bank of Cleveland or the Federal Reserve System.

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. https://doi.org/10.26509/frbc-wp-199711