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Accuracy, Speed and Robustness of Policy Function Iteration

Listed author(s):
  • Alexander Richter

    ()

  • Nathaniel Throckmorton

    ()

  • Todd Walker

    ()

Policy function iteration methods for solving and analyzing dynamic stochastic general equilibrium models are powerful from a theoretical and computational perspective. Despite obvious theoretical appeal, significant startup costs and a reliance on grid-based methods 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. Within the class of policy function iteration methods, we advocate using time iteration with linear interpolation. 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 on the nominal interest rate to highlight the attractiveness of our methodology. We compare our advocated approach to other familiar iteration and approximation methods, highlighting the tradeoffs between accuracy, speed and robustness. Copyright Springer Science+Business Media New York 2014

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File URL: http://hdl.handle.net/10.1007/s10614-013-9399-2
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Article provided by Springer & Society for Computational Economics in its journal Computational Economics.

Volume (Year): 44 (2014)
Issue (Month): 4 (December)
Pages: 445-476

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Handle: RePEc:kap:compec:v:44:y:2014:i:4:p:445-476
DOI: 10.1007/s10614-013-9399-2
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