Publication Type

Journal Article

Publication Date

8-2012

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 Hajek-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. (C) 2012 Elsevier B.V. All rights reserved.

Keywords

Bayesian asymptotics, Asymptotic normality, Local asymptotic normality, Locally asymptotic quadratic, Optimality property of MLE, Weak convergence

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

169

Issue

2

First Page

258

Last Page

265

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2012.01.025

Publisher

Elsevier

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1016/j.jeconom.2012.01.025

Included in

Econometrics Commons

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