Bravo, Francesco orcid.org/0000-0002-8034-334X, Li, Degui orcid.org/0000-0001-6802-308X and Tjostheim, Dag (2021) Robust Nonlinear Regression Estimation in Null Recurrent Time Series. Journal of Econometrics. pp. 416-438. ISSN 0304-4076
Abstract
In this article, we study parametric robust estimation in nonlinear regression models with regressors generated by a class of non-stationary and null recurrent Markov process. The nonlinear regression functions can be either integrable or asymptotically homogeneous, covering many commonly-used functional forms in parametric nonlinear regression. Under regularity conditions, we derive both the consistency and limit distribution results for the developed general robust estimators (including the nonlinear least squares, least absolute deviation and Huber’s M-estimators). The convergence rates of the estimation depend on not only the functional form of nonlinear regression, but also on the recurrence rate of the Markov process. Some Monte-Carlo simulation studies are conducted to examine the numerical performance of the proposed estimators and verify the established asymptotic properties in finite samples. Finally two empirical applications illustrate the usefulness of the proposed robust estimation method.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Mathematics (York) The University of York > Faculty of Social Sciences (York) > Economics and Related Studies (York) |
Depositing User: | Pure (York) |
Date Deposited: | 10 Jun 2020 11:10 |
Last Modified: | 17 Dec 2024 00:16 |
Published Version: | https://doi.org/10.1016/j.jeconom.2020.03.028 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.jeconom.2020.03.028 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161741 |