The Annals of Statistics

On Estimation of a Regression Model with Long-Memory Stationary Errors

Yoshihiro Yajima

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Abstract

We consider estimation of a regression model with long-memory stationary errors. First, we estimate the regression parameters by the least-squares estimator (LSE) and, next, those describing the correlation structure of the error terms by using the residuals obtained from the LSE. Certain regularity conditions introduced to develop the asymptotic theory no longer hold in this model. In this situation we derive asymptotic properties of the preceding estimation procedure.

Article information

Source
Ann. Statist., Volume 16, Number 2 (1988), 791-807.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176350837

Digital Object Identifier
doi:10.1214/aos/1176350837

Mathematical Reviews number (MathSciNet)
MR947579

Zentralblatt MATH identifier
0661.62090

JSTOR
links.jstor.org

Subjects
Primary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
Secondary: 62J05: Linear regression

Keywords
Long-memory models regression least-squares estimators

Citation

Yajima, Yoshihiro. On Estimation of a Regression Model with Long-Memory Stationary Errors. Ann. Statist. 16 (1988), no. 2, 791--807. doi:10.1214/aos/1176350837. https://projecteuclid.org/euclid.aos/1176350837


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