Fast indirect robust generalized method of moments
The Robust Generalized Methods of Moments (RGMM) and the Indirect Robust GMM (IRGMM) are algorithms for estimating parameter values in statistical models, such as diffusion models for interest rates, in a robust way. The long computation time is one of the main challenges facing these methods. In this paper, we introduce accelerated variants of RGMM and IRGMM. The fixed point iteration in RGMM is accelerated using minimal polynomial extrapolation, and the simulation of pseudo-observations in IRGMM is sped up by using a higher order stochastic Runge-Kutta method. We illustrate the fast performance of these algorithms for an interest rate diffusion model on four datasets.
Year of publication: |
2009
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Authors: | Loisel, Sébastien ; Takane, Marina |
Published in: |
Computational Statistics & Data Analysis. - Elsevier, ISSN 0167-9473. - Vol. 53.2009, 10, p. 3571-3579
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Publisher: |
Elsevier |
Saved in:
Online Resource
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