EQUIVALENCE OF THE HIGHER ORDER ASYMPTOTIC EFFICIENCY OF k-STEP AND EXTREMUM STATISTICS
Donald Andrews ()
Econometric Theory, 2002, vol. 18, issue 5, 1040-1085
It is well known that a one-step scoring estimator that starts from any N1/2-consistent estimator has the same first-order asymptotic efficiency as the maximum likelihood estimator. This paper extends this result to k-step estimators and test statistics for k â‰¥ 1, higher order asymptotic efficiency, and general extremum estimators and test statistics.The paper shows that a k-step estimator has the same higher order asymptotic efficiency, to any given order, as the extremum estimator toward which it is stepping, provided (i) k is sufficiently large, (ii) some smoothness and moment conditions hold, and (iii) a condition on the initial estimator holds.For example, for the Newtonâ€“Raphson k-step estimator based on an initial estimator in a wide class, we obtain asymptotic equivalence to integer order s provided 2k â‰¥ s + 1. Thus, for k = 1, 2, and 3, one obtains asymptotic equivalence to first, third, and seventh orders, respectively. This means that the maximum differences between the probabilities that the (N1/2-normalized) k-step and extremum estimators lie in any convex set are o(1), o(Nâˆ’3/2), and o(Nâˆ’3), respectively.
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Working Paper: Equivalence of the Higher-order Asymptotic Efficiency of k-step and Extremum Statistics (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:cup:etheor:v:18:y:2002:i:05:p:1040-1085_18
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