Includes bibliographical references.
|Series||IMF working paper -- WP/96/106|
|Contributions||International Monetary Fund.|
|The Physical Object|
|Pagination||30 p. ;|
|Number of Pages||30|
Title: A Robust and efficient method for solving nonlinear rational expectations models Created Date: 8/4/ PMCited by: 5. Downloadable! The development and use of forward-looking macro models in policymaking institutions has proceeded at a pace much slower than predicted in the early s. An important reason is that researchers have not had access to robust and efficient solution techniques for solving nonlinear forward-looking models. This paper discusses the properties of a new algorithm that is used for. A Robust and Efficient Method for Solving Nonlinear Rational Expectations Models Prepared by Michel Juillard and Douglas Laxton1 Authorized for distribution by Peter Isard September Abstract The development and use of forward-looking macro models in policymaking institutions has proceeded at a pace much slower than predicted in the early. The second part presents methods for solving dynamic stochastic models in economics and finance, including dynamic programming, rational expectations, and arbitrage pricing models in discrete and.
Methods for solving non‐linear, rational expectations business cycle models A 1‐day worskhopfor the Bath‐Bristol‐Exeter Doctoral Training Centre, by Tony Yates University of Bristol +Centre for Macroeconomics 21 May 21‐ Solving Non-Linear Rational Expectations Models This paper documents the set of Matlab based tools for solving nonlinear ra-tional expectations models discussed in Fackler. A general framework for rational expectations models is discussed in the next section and illustrated with two exam-ples. In the following section solution approaches are. The iterative method for solving MONASH under rational expectations The iterative method developed to solve the MONASH model (or other large models) under rational expectations is documented in section 21 of Dixon and Rimmer () and, in more detail, in sections and 44 of Dixon and Rimmer (). We describe methods for solving general linear rational expectations models in continuous or discrete timing with or without exogenous variables. The methods .
Solving Linear Rational Expectations Models A computationally robust solution method for linear rational expectations models is displayed, based on the QZ matrix decomposition. Any rational expectations model, in continuous or discrete time, can be solved by this approach. Fair, R C and Taylor, J B (), “Solution and Maximum Likelihood Estimation of Dynamic Non-Linear Rational Expectation Models”, Econometrica, Vol. 51, pp. – CrossRef Google Scholar Fisher, P G, Holly, S and Hughes Hallett A J (), “Efficient Solution Techniques for Dynamic Nonlinear Rational Expectations Models”, J. Econom. Corrections. All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:vyipSee general information about how to correct material in RePEc.. For technical questions regarding this item, or to correct its authors, title. This paper presents efficient methods for the solution of finite-horizon multivariate linear rational expectations (MLRE) models, linking the solution of such models to the problem of solving sparse linear equation systems with a block-tridiagonal coefficient matrix structure. Two numerical schemes for the solution of this type of equation systems are discussed, and it is shown how these.