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A class of Minimum Distance Estimators in Markovian Multiplicative Error Models

Author

Listed:
  • Hira L. Koul

    (Michigan State University)

  • Indeewara Perera

    (The University of Sheffield)

  • Narayana Balakrishna

    (Cochin University of Science Technology)

Abstract

This paper proposes a class of minimum distance estimators for the underlying parameters in a Markovian parametric multiplicative error time series model. This class of estimators is based on the integrals of the square of a certain marked residual process. The paper derives the asymptotic distributions of the proposed estimators. In a finite sample comparison, some members of the proposed class of estimators dominate a generalized method of moments estimator in terms of the finite sample bias at a variety of chosen error distributions while neither dominate each other in terms of the mean squared error at these error distributions. A real data example is considered to illustrate the proposed estimation procedures.

Suggested Citation

  • Hira L. Koul & Indeewara Perera & Narayana Balakrishna, 2023. "A class of Minimum Distance Estimators in Markovian Multiplicative Error Models," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 87-115, May.
    • Handle: RePEc:spr:sankhb:v:85:y:2023:i:1:d:10.1007_s13571-021-00274-x
    DOI: 10.1007/s13571-021-00274-x
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    Keywords

    Marked empirical process; GMM estimator;

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