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LEAST SQUARES ESTIMATION FOR NONLINEAR REGRESSION MODELS WITH HETEROSCEDASTICITY

Published online by Cambridge University Press:  11 January 2021

Qiying Wang
Qiying Wang*
Affiliation:
The University of Sydney
*
Address correspondence to Qiying Wang, School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia; e-mail: qiying.wang@sydney.edu.au
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Abstract

This paper develops an asymptotic theory of nonlinear least squares estimation by establishing a new framework that can be easily applied to various nonlinear regression models with heteroscedasticity. As an illustration, we explore an application of the framework to nonlinear regression models with nonstationarity and heteroscedasticity. In addition to these main results, this paper provides a maximum inequality for a class of martingales, which is of interest in its own right.

Information

Type
MISCELLANEA
Information
Econometric Theory , Volume 37 , Issue 6 , December 2021 , pp. 1267 - 1289
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

The author thanks Professor Peter Phillips (Editor), Professor Giuseppe Cavaliere (Co-Editor) and four referees for their very helpful comments on the original version. The author also acknowledges the research support given by the Australian Research Council.

References

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