Document Type
Discussion Paper
Publication Date
1-1-2010
CFDP Number
1748
CFDP Pages
19
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.
Recommended Citation
Ploberger, Werner and Phillips, Peter C.B., "Optimal Estimation under Nonstandard Conditions" (2010). Cowles Foundation Discussion Papers. 2076.
https://elischolar.library.yale.edu/cowles-discussion-paper-series/2076