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 Cram r-Rao inequality, Cholesky decomposition, missing observations and numerical optimization. A simple geometrical approach is used.
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