Mean Variance Optimization of Forward Looking Systems and Worst-case Analysis
AbstractIn this paper we consider expected value and mean variance optimization of a general forward--looking stochastic model. The problem is transformed into a general--nonlinear programming problem by adding extra constraints, which restrict the policy maker to commit to a certain policy. Based on this policy,and the rest of the economic structure, the agents can forecast future states except for random future disturbances. We present algorithms for computing optimal expected values based on iterative Taylor expansion and an interior point method for computing minimax robust policies. The results from both approaches are compared in order to assess the relative advantage of each approach and measure robustness against performance, and are also compared against DYNARE - a program for solving rational expectations models
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2005 with number 267.
Date of creation: 11 Nov 2005
Date of revision:
macroeconomic policy; optimization; uncertain models;
Find related papers by JEL classification:
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
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