Higher-Order Solutions to Dynamic, Discrete-Time Rational Expectations Models: Methods and an Application to Optimal Monetary Policy
AbstractWe 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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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2003 with number 64.
Date of creation: 01 Aug 2003
Date of revision:
perturbation analysis; optimal monetary policy;
Other versions of this item:
- Eric Swanson & Gary Anderson & Andrew Levin, 2004. "Higher-Order Solutions to Dynamic, Discrete-Time Rational Expectations Models: Methods and an Application to Optimal Monetary Policy," Econometric Society 2004 North American Winter Meetings 576, Econometric Society.
- 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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- Paul Pichler, 2005. "Evaluating Approximate Equilibria of Dynamic Economic Models," Vienna Economics Papers 0510, University of Vienna, Department of Economics.
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