A class of Minimum Distance Estimators in Markovian Multiplicative Error Models

Hira L. Koul, Indeewara Perera and Narayana Balakrishna ()
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Hira L. Koul: Michigan State University
Indeewara Perera: The University of Sheffield
Narayana Balakrishna: Cochin University of Science Technology

Sankhya B: The Indian Journal of Statistics, 2023, vol. 85, issue 1, No 3, 87-115

Abstract: 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.

Keywords: Marked empirical process; GMM estimator; Primary 62M05; Secondary 62M10 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13571-021-00274-x

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