Abstract
We have already seen that various typical estimators and tests have the same limiting behavior. Thus it is required to illuminate their distinction. In this chapter, using higher order approximations (Edgeworth expansions) of the distribution of estimators and tests we discuss their higher order asymptotic optimality.
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© 2000 Springer Science+Business Media New York
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Taniguchi, M., Kakizawa, Y. (2000). Higher Order Asymptotic Theory for Stochastic Processes. In: Asymptotic Theory of Statistical Inference for Time Series. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1162-4_4
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DOI: https://doi.org/10.1007/978-1-4612-1162-4_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-7028-7
Online ISBN: 978-1-4612-1162-4
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