The widely used Cochrane-Orcutt and Hildreth-Lu procedures for estimating the parameters of a linear regression model with first-order serial correlation typically ignore the first observation. An alternative maximum likelihood procedure is recommended in this paper. This procedure is preferable...

Maximum likelihood estimation of equation systems with first-order autocorrelation should, in principle, take into account the first observation and associated stationarity condition. In the general case, this leads to computational difficulties compared with conventional procedures, which...

This paper develops a technique for estimating linear models with second-order autoregressive errors, which utilizes the full set of observations, and explicitly constrains the estimates of the error process to satisfy a priori stationarity conditions. A nonlinear solution technique which is new...