M-estimation for regressions with integrated regressors and arma errors

被引：0

作者：

Shin, DW ^{[1
]}

Lee, O ^{[1
]}

机构：

[1] Ewha Womans Univ, Dept Stat, Seoul 120750, South Korea

来源：

JOURNAL OF TIME SERIES ANALYSIS | 2004年 / 25卷 / 02期

关键词：

ARMA process; efficiency; endogeneity; fully modified estimator; M-estimation; serial correlation;

D O I：

10.1046/j.0143-9782.2003.00350.x

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

General M-estimation is developed for regression models with integrated regressors and autoregressive moving average (ARMA) errors, in which the ARMA parameters are jointly estimated with the regression parameters. The large sample distribution of the M-estimator is derived. Allowing the regressors to be dependent on the error terms, a parametric 'fully modified' (FM) M-estimator is proposed. In cases of ARMA errors, a Monte-Carlo experiment reveals superiority of the parametric estimators over the semiparametric FM M-estimator of Phillips Econometric Theory 11 (1995, p 912) in terms of empirical mean squared error.

引用

页码：283 / 299

页数：17

共 25 条

[1] HETEROSKEDASTICITY AND AUTOCORRELATION CONSISTENT COVARIANCE-MATRIX ESTIMATION [J].