Sparse direct methods for model simulation

Article Abstract:

Newton-type sparse direct methods offer valuable solutions for the simulation and evaluation of large macroeconomic models with forward-looking variables. These methods actually involve subdividing a stacked model into recursive submodels and finding the solution through the help of Newton algorithm. However, the model's original block pattern must not be altered during the process. Such methods are highly efficient under a linear system wherein an LU factorization is used. Other solution methods, particularly the Gauss-Seidel algorithm may also be used in determining the L and U matrices.

Economics, Models, Analysis, Evaluation, Macroeconomics, Recursive functions, Linear systems

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Krylov methods for solving models with forward-looking variables

Article Abstract:

An analysis of Krylov method use in simulations of large macroeconomic models with forward-looking variables reveals that the methods feature low computational complexity and low storage requirements. The study determines an alternative to the exact Newton method with sparse Gaussian elimination and finds that inexact Newton algorithms using Krylov methods viable alternatives.

Econometrics & Model Building, Management Science, Business models

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An algorithm competition: first-order iterations versus Newton-based techniques

Article Abstract:

A comparison of Newton-based techniques and first order iterations used in solving nonlinear forward-looking models reveals that Newton-based techniques are considerably faster and less prone to simulation failure. The study compares the two techniques by solving them on MULTIMOD and finds that the Newton-Raphson iterative method is better than the Fair-Taylor algorithm.

Research and Development in the Physical, Engineering, and Life Sciences, Statistics, Statistics (Mathematics)

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