JSM 2014 Home
Online Program Home
My Program

Abstract Details

Activity Number: 300
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Education
Abstract #313427 View Presentation
Title: Inference for Linear Regression with Autocorrelated Errors: Why Cochrane Orcutt Procedure Should Not Be Recommended
Author(s): Tharanga Wickramarachchi*+ and Colin Gallagher and Jeremy Brown
Companies: Georgia Southern University and Clemson University and American Credit Acceptance
Keywords: Cochrane Orcutt prcedure ; Ordinary least squares estimator ; type I error ; autocorrelation ; Mean squared error ; bias correction

Positive autocorrelation can inflate type I error in tests for significance of the linear regression slope parameter in time series data. The ordinary least squares (OLS) regression parameter estimators are not best linear unbiased estimators in the presence of autocorrelation. In practice it is necessary to estimate both correlation and regression parameters. This process can result in estimators with larger mean squared error (MSE) than that of the OLS estimator. Popular textbooks recommend Cochrane Orcutt (CO) procedure as a better method to estimate the slope coefficient in these settings. For smaller samples, we observe tests carried out based CO estimates for significance of the slope parameter provide unacceptably high type I error probabilities. In a model with linear trend and first order autoregressive errors, the OLS estimator of slope has competitive MSE relative to other procedures. Using bias corrected estimates of correlation, we improve the estimate of standard error of the OLS estimated slope. Equivalent degrees of freedom calculated from these bias corrected correlation estimators are used to stabilize the type I error rate of tests significance for the slope.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2014 program

2014 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Professional Development program, please contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.