Hi-Stat img
□ ENGLISH
□ HOME
□ プロジェクト概要
組織図

概念図

スタッフ
□ 研究成果
ディスカッションペーパー

データベース
□ お知らせ
公募情報

研究会日程

過去の研究会と報告資料

レクチャーシリーズ

過去のレクチャーと報告資料

ニュースレター
□ リンク
一橋大学

一橋大学附属図書館

一橋大学経済研究所

社会科学統計情報研究センター

アジア長期経済統計プロジェクト

Global Economic History Network

政府統計ミクロデータの試行的提供

ICPSR データアーカイブ

AMU and AMU Deviation indicators

Asymptotic Properties of the Efficient Estimators for
Cointegrating Regression Models with Serially Dependent Errors



Eiji Kurozumi and Kazuhiko Hayakawa


December, 2006


Previous paper Next paper
Abstract
In this paper, we analytically investigate three efficient estimators for cointegrating regression models: Phillips and Hansen's (1990) fully modified OLS estimator, Park's (1992) canonical cointegrating regression estimator, and Saikkonen's (1991) dynamic OLS estimator. First, by the Monte Carlo simulations, we demonstrate that these efficient methods do not work well when the regression errors are strongly serially correlated. In order to explain this result, we assume that the regression errors are generated from a nearly integrated autoregressive (AR) process with the AR coefficient approaching 1 at a rate of 1/T , where T is the sample size. We derive the limiting distributions of the three efficient estimators as well as the OLS estimator and show that they have the same limiting distribution under this assumption. This implies that the three efficient methods no longer work well when the regression errors are strongly serially correlated. Further, we consider the case where the AR coefficient in the regression errors approaches 1 at a rate slower than 1/T . In this case, the limiting distributions of the efficient estimators depend on the approaching rate. If the rate is slow enough, the efficiency is established for the three estimators; however, if the approaching rate is relatively fast, they have the same limiting distribution as the OLS estimator. This result explains why the effect of the efficient methods diminishes as the serial correlation in the regression errors gets stronger.
Download (485KB)
Copyright (C) 2003-2007 by Institute of Economic Research.All rights reserved.