Koul, H.L., Perera, I. and Balakrishna, N. (2022) A class of minimum distance estimators in Markovian multiplicative error models. Sankhya B. ISSN 0976-8386
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.
Metadata
Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021, Indian Statistical Institute. This is an author-produced version of a paper subsequently published in Sankhya B. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Marked empirical process; GMM estimator |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Economics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Feb 2022 16:25 |
Last Modified: | 28 Jan 2023 01:13 |
Status: | Published online |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | https://doi.org/10.1007/s13571-021-00274-x |