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Identifier to cite or link to this item: https://hdl.handle.net/10086/19420

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Title
Asymptotic Efficiency of the OLS Estimator with Singular Limiting Sample Moment Matrices
Creator
Creator Name
Uematsu, Yoshimasa Uematsu, Yoshimasa
Affiliation
Affiliation Name
Hitotsubashi University
Editor
Contributor Name
社会科学の高度統計・実証分析拠点構築 = Research Unit for Statistical and Empirical Analysis in Social Sciences
Affiliation
Affiliation Name
Hitotsubashi University
Abstract
This paper presents a time series model that has an asymptotically efficient ordinary least squares (OLS) estimator, irrespective of the singularity of its limiting sample moment matrices. In the literature on stationary time series analysis, Grenander and Rosenblatt's (1957) (G-R) classical result is used to judge the asymptotic efficiency of regression coefficients on deterministic regressors satisfying Grenander's condition. Without this condition, however, it is not obvious that the model is efficient. In this paper, we introduce such a model by proving the efficiency of the model with a slowly varying (SV) regressor under the same condition on error terms constrained in G-R. This kind of regressor is known to display asymptotic singularity in the sample moment matrices, as in Phillips (2007), such that Grenander's condition fails.
Description
October 2011
Publisher
Institute of Economic Research, Hitotsubashi University
Issued Date
2011-10
Language
English(eng)
Resource Type
technical report
Version Type
VoR
Is Part OfRelation -
Related Title
Global COE Hi-Stat Discussion Paper Series ; No. 208
Relation
Related Identifier
URI
  http://gcoe.ier.hit-u.ac.jp/english/index.html
Number of Pages
20
Sponsorship
グローバルCOEプログラム = Global COE Program
Appears in Collections
Global COE Hi-Stat Discussion Paper Series
URL
https://hdl.handle.net/10086/19420