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Higher-Order Stochastic Expansions and Approximate Moments for Non-linear Models with Heterogeneous Observations

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  • Paul Rilstone

    (York University)

Abstract

Higher-order asymptotic theory for estimators of the parameters of non-linear models with heterogeneous observations is developed. New methods for deriving stochastic expansions and approximate first through fourth moments of these estimators are presented.

Suggested Citation

  • Paul Rilstone, 2021. "Higher-Order Stochastic Expansions and Approximate Moments for Non-linear Models with Heterogeneous Observations," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 99-120, December.
    • Handle: RePEc:spr:jqecon:v:19:y:2021:i:1:d:10.1007_s40953-021-00265-9
    DOI: 10.1007/s40953-021-00265-9
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    References listed on IDEAS

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    1. Rilstone, Paul & Srivastava, V. K. & Ullah, Aman, 1996. "The second-order bias and mean squared error of nonlinear estimators," Journal of Econometrics, Elsevier, vol. 75(2), pages 369-395, December.
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    Cited by:

    1. Yong Bao & Aman Ullah, 2021. "The Special Issue in Honor of Anirudh Lal Nagar: An Introduction," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 1-8, December.

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    More about this item

    Keywords

    Stochastic approximations; Approximate moments; Non-linear;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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