Abstract
A nonlinear regression modelxt=gt(θ0)+ et,t⩾1, is considered. Under a number of conditions on its elements et and gt(θ0) it is proved that the distribution of the normalized least square estimate of the parameter θ0 converges uniformly on the real axis to the standard normal law at least as quickly as a quantity of the order T−1/2 as T → ∞, where T is the size of the sample, by which the estimate is formed.
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