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Higher-Order Solutions to Dynamic, Discrete-Time Rational Expectations Models: Methods and an Application to Optimal Monetary Policy

Author

Listed:
  • Eric Swanson
  • Gary Anderson
  • Andrew Levin

Abstract

We present an algorithm and software routines for computing nth-order approximate solutions to dynamic, discrete-time rational expectations models around a nonstochastic steady state. We apply these routines to investigate the optimal monetary policy with commitment (and from a ``timeless perspective'') in an optimizing-agent model with nominal price rigidities, subject to a fiscal policy that is stochastic, suboptimal, and exogenous to the central bank
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Suggested Citation

  • Eric Swanson & Gary Anderson & Andrew Levin, 2003. "Higher-Order Solutions to Dynamic, Discrete-Time Rational Expectations Models: Methods and an Application to Optimal Monetary Policy," Computing in Economics and Finance 2003 64, Society for Computational Economics.
    • Handle: RePEc:sce:scecf3:64
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    Cited by:

    1. Paul Pichler, 2007. "Forecasting with estimated dynamic stochastic general equilibrium models: The role of nonlinearities," Vienna Economics Papers vie0702, University of Vienna, Department of Economics.
    2. Paul Pichler, 2005. "Evaluating Approximate Equilibria of Dynamic Economic Models," Vienna Economics Papers 0510, University of Vienna, Department of Economics.

    More about this item

    Keywords

    perturbation analysis; optimal monetary policy;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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