Computational Economics, Vol. 44, Issue 4, 2014
35 Pages Posted: 20 Feb 2013 Last revised: 29 Jun 2015
Date Written: August 28, 2013
Policy function iteration methods for solving and analyzing dynamic, stochastic general equilibrium models are powerful from both a theoretical and computational perspective. Despite obvious theoretical appeal, significant startup costs and a reliance on a grid-based method have limited the use of policy function iteration as a solution algorithm. We reduce these costs by providing a user-friendly suite of MATLAB functions that introduce multi-core processing and Fortran via MATLAB's executable function. We demonstrate why policy function iteration is particularly useful in solving models with regime-dependent parameters, recursive preferences, and binding constraints. We examine a canonical real business cycle model and a new Keynesian model that features regime switching in policy parameters, Epstein-Zin preferences, and monetary policy that occasionally hits the zero-lower bound to highlight the attractiveness of our methodology. We compare our advocated approach to other familiar computational methods, highlighting the tradeoffs between accuracy and speed.
Keywords: Policy Function Iteration, Zero-Lower Bound, Epstein-Zin preferences
JEL Classification: C63, C68, E52, E62
Suggested Citation: Suggested Citation
Richter, Alexander W. and Throckmorton, Nathaniel A. and Walker, Todd B., Accuracy, Speed and Robustness of Policy Function Iteration (August 28, 2013). Computational Economics, Vol. 44, Issue 4, 2014. Available at SSRN: https://ssrn.com/abstract=2220235 or http://dx.doi.org/10.2139/ssrn.2220235