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Robust Nonlinear Regression Estimation in Null Recurrent Time Series

Research output: Contribution to journalArticle

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Robust Nonlinear Regression Estimation in Null Recurrent Time Series. / Bravo, Francesco; Li, Degui; Tjostheim, Dag.

In: Journal of Econometrics, 24.03.2020.

Research output: Contribution to journalArticle

Harvard

Bravo, F, Li, D & Tjostheim, D 2020, 'Robust Nonlinear Regression Estimation in Null Recurrent Time Series', Journal of Econometrics.

APA

Bravo, F., Li, D., & Tjostheim, D. (Accepted/In press). Robust Nonlinear Regression Estimation in Null Recurrent Time Series. Journal of Econometrics.

Vancouver

Bravo F, Li D, Tjostheim D. Robust Nonlinear Regression Estimation in Null Recurrent Time Series. Journal of Econometrics. 2020 Mar 24.

Author

Bravo, Francesco ; Li, Degui ; Tjostheim, Dag. / Robust Nonlinear Regression Estimation in Null Recurrent Time Series. In: Journal of Econometrics. 2020.

Bibtex - Download

@article{afb4a4bd9028428f840aafb22e975950,
title = "Robust Nonlinear Regression Estimation in Null Recurrent Time Series",
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{\textquoteright}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.",
author = "Francesco Bravo and Degui Li and Dag Tjostheim",
note = "Date of acceptance: 24 March 2020.",
year = "2020",
month = mar,
day = "24",
language = "English",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Robust Nonlinear Regression Estimation in Null Recurrent Time Series

AU - Bravo, Francesco

AU - Li, Degui

AU - Tjostheim, Dag

N1 - Date of acceptance: 24 March 2020.

PY - 2020/3/24

Y1 - 2020/3/24

N2 - 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.

AB - 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.

M3 - Article

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

ER -