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Improved Estimation Strategy in Multi-Factor Vasicek Model

In: Statistical Inference, Econometric Analysis and Matrix Algebra

Author

Listed:
  • S. Ejaz Ahmed

    (University of Windsor, Department of Mathematics and Statistics)

  • Sévérien Nkurunziza

    (University of Windsor, Department of Mathematics and Statistics)

  • Shuangzhe Liu

    (University of Canberra, Faculty of Information Sciences and Engineering)

Abstract

We consider simultaneous estimation of the drift parameters of multivari-ate Ornstein-Uhlebeck process. In this paper, we develop an improved estimation methodology for the drift parameters when homogeneity of several such parameters may hold. However, it is possible that the information regarding the equality of these parameters may not be accurate. In this context, we consider Stein-rule (or shrinkage) estimators to improve upon the performance of the classical maximum likelihood estimator (MLE). The relative dominance picture of the proposed estimators are explored and assessed under an asymptotic distributional quadratic risk criterion. For practical arguments, a simulation study is conducted which illustrates the behavior of the suggested method for small and moderate length of time observation period. More importantly, both analytical and simulation results indicate that estimators based on shrinkage principle not only give an excellent estimation accuracy but outperform the likelihood estimation uniformly.

Suggested Citation

  • S. Ejaz Ahmed & Sévérien Nkurunziza & Shuangzhe Liu, 2009. "Improved Estimation Strategy in Multi-Factor Vasicek Model," Springer Books, in: Bernhard Schipp & Walter Kräer (ed.), Statistical Inference, Econometric Analysis and Matrix Algebra, pages 255-270, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2121-5_17
    DOI: 10.1007/978-3-7908-2121-5_17
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