By the same authors

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Robust Nonlinear Regression Estimation in Null Recurrent Time Series

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Publication details

JournalJournal of Econometrics
DateAccepted/In press - 24 Mar 2020
DateE-pub ahead of print - 4 Dec 2020
DatePublished (current) - 1 Oct 2021
Issue number2
Number of pages23
Pages (from-to)416-438
Early online date4/12/20
Original languageEnglish


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.

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© 2020 Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

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