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Least Squares Estimation For Nonlinear Regression Models With Heteroscedasticity

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  • Wang, Qiying

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.

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  • Wang, Qiying, 2021. "Least Squares Estimation For Nonlinear Regression Models With Heteroscedasticity," Econometric Theory, Cambridge University Press, vol. 37(6), pages 1267-1289, December.
    • Handle: RePEc:cup:etheor:v:37:y:2021:i:6:p:1267-1289_7
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    Cited by:

    1. Mayer, Alexander, 2023. "Two-step estimation in linear regressions with adaptive learning," Statistics & Probability Letters, Elsevier, vol. 195(C).
    2. Alexander Mayer, 2022. "Two-step estimation in linear regressions with adaptive learning," Papers 2204.05298, arXiv.org, revised Nov 2022.

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