Linear Models with Correlated Disturbances

This book is primarily concerned with the estimation of regression models with correlated disturbances. Topics discussed include maximum likelihood, test strategies, Kalman filtering, conditional normal distributions, the Cramér-Rao inequality, Cholesky decomposition, missing observations and numerical optimization. A simple geometrical approach is used.