Skip Navigation

Biometrika 1971 58(2):299-312; doi:10.1093/biomet/58.2.299
© 1971 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by PIERCE, D. A.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Least squares estimation in the regression model with autoregressive-moving average errors

DAVID A. PIERCE {dagger}

University of Missouri

To treat the problem of correlated errors in regression, a model in which the errors follow a stationary autoregressive-moving average time series is suggested. Simultaneous least squares estimation of the regression and the time series parameters is discussed, and it is shown that asymptotically the estimates obtained in this manner possess normal distributions, whether or not the errors themselves are normally distributed. The estimates of the regression parameters are uncorrelated with those of the time series parameters; the former are distributed as if they had arisen from a certain transformed model with uncorrelated errors, while the latter have the same covariance matrix as those from a stationary series with no deterministic component. The estimate of variance is also asymptotically normal. A Monte Carlo sampling study indicates that these results can serve as a useful approximation for samples of moderate size.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Journal of the Academy of Marketing ScienceHome page
A. J. Adams
Using the Calendar to Improve Sales Forecasts
Journal of the Academy of Marketing Science, June 1, 1984; 12(3): 103 - 112.



Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.