Abstract
We revisit the second-order nonlinear least square estimator proposed in Wang and Leblanc (Anne Inst Stat Math 60:883–900, 2008) and show that the estimator reaches the asymptotic optimality concerning the estimation variability. Using a fully semiparametric approach, we further modify and extend the method to the heteroscedastic error models and propose a semiparametric efficient estimator in this more general setting. Numerical results are provided to support the results and illustrate the finite sample performance of the proposed estimator.
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Kim, M., Ma, Y. The efficiency of the second-order nonlinear least squares estimator and its extension. Ann Inst Stat Math 64, 751–764 (2012). https://doi.org/10.1007/s10463-011-0332-y
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DOI: https://doi.org/10.1007/s10463-011-0332-y