Linear models with correlated disturbances
This text provides a summary of new and old estimation techniques for regression models with disturbances that follow an autoregressive-moving average process. Test strategies for discriminating between various types of autocorrelation are also discussed. Alternative proofs and some extensions of well known theory are provided, partly by using the geometry of linear spaces spanned by scalar random variables. Apart from minor revisions this text is identical with the authors Ph. D. thesis.
scientific article; zbMATH DE number 108228