Optimal Estimation under Nonstandard Conditions

Werner Ploberger and Peter Phillips

No 1748, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: We analyze optimality properties of maximum likelihood (ML) and other estimators when the problem does not necessarily fall within the locally asymptotically normal (LAN) class, therefore covering cases that are excluded from conventional LAN theory such as unit root nonstationary time series. The classical Hájek-Le Cam optimality theory is adapted to cover this situation. We show that the expectation of certain monotone "bowl-shaped" functions of the squared estimation error are minimized by the ML estimator in locally asymptotically quadratic situations, which often occur in nonstationary time series analysis when the LAN property fails. Moreover, we demonstrate a direct connection between the (Bayesian property of) asymptotic normality of the posterior and the classical optimality properties of ML estimators.

Keywords: Bayesian asymptotics; Asymptotic normality; Local asymptotic normality; Locally asymptotic quadratic; Optimality property of MLE; Weak convergence (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2010-01
New Economics Papers: this item is included in nep-ecm
Note: CFP 1364
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Published in Journal of Econometrics (August 2012), 169(2): 258-265

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