An extension of cochran-orcutt procedure for generalized linear regression models with periodically correlated errors

An important assumption of ordinary regression models is independence among errors. This research considers the case of periodically correlated errors following the periodic AR model of order 1 (PAR(1)). The remedial measure for correlated errors in regression known as the Cochran-Orcutt procedure is generalized to the case of periodically correlated errors. The motivation for making such generalizations is that the response data may inhibit some seasonality, which may not be captured by the traditional AR(1) autoregressive model. The proposed procedure is described and the bias and MSE of the resulting intercept and slope parameter estimates of the simple LR model with errors following PAR(1) are compared with those of ordinary least squares (OLS) estimates via simulation. An application of real data is provided. © 2012 JMASM, Inc.