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Working Paper
OccBin: A Toolkit for Solving Dynamic Models With Occasionally Binding Constraints Easily
We describe how to adapt a first-order perturbation approach and apply it in a piecewise fashion to handle occasionally binding constraints in dynamic models. Our examples include a real business cycle model with a constraint on the level of investment and a New Keynesian model subject to the zero lower bound on nominal interest rates. We compare the piecewise linear perturbation solution with a high-quality numerical solution that can be taken to be virtually exact. The piecewise linear perturbation method can adequately capture key properties of the models we consider. A key advantage of ...
Working Paper
Approximately Right?: Global v. Local Methods for Open-Economy Models with Incomplete Markets
Global and local methods are widely used in international macroeconomics to analyze incomplete-markets models. We study solutions for an endowment economy, an RBC model and a Sudden Stops model with an occasionally binding credit constraint. First-order, second-order, risky steady state and DynareOBC solutions are compared v. fixed-point-iteration global solutions in the time and frequency domains. The solutions differ in key respects, including measures of precautionary savings, cyclical moments, impulse response functions, financial premia and macro responses to credit constraints, and ...
Working Paper
A Generalized Time Iteration Method for Solving Dynamic Optimization Problems with Occasionally Binding Constraints
We study a generalized version of Coleman (1990)’s time iteration method (GTI) for solving dynamic optimization problems. Our benchmark framework is an irreversible investment model with labor-leisure choice. The GTI algorithm is simple to implement and provides advantages in terms of speed relative to Howard (1960)’s improvement algorithm. A second application on a heterogeneous-agents incomplete-markets model further explores the performance of GTI.
Working Paper
Likelihood Evaluation of Models with Occasionally Binding Constraints
Applied researchers interested in estimating key parameters of DSGE models face an array of choices regarding numerical solution and estimation methods. We focus on the likelihood evaluation of models with occasionally binding constraints. We document how solution approximation errors and likelihood misspecification, related to the treatment of measurement errors, can interact and compound each other.