Linear Models with Correlated Disturbances

By:

Paul Knottnerus

Format:

Paperback / softback

Series:

Lecture Notes in Economics and Mathematical Systems

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Description

The main aim of this volume is to give a survey of new and old estimation techniques for regression models with correlated disturbances, especially with autoregressive-moving average disturbances. In nearly all chapters the usefulness of the simple geometric interpretation of the classical ordinary Least Squares method is demonstrated. It emerges that both well-known and new results can be derived in a simple geometric manner, e.g., the conditional normal distribution, the Kalman filter equations and the Cramer-Rao inequality. The same geometric interpretation also shows that disturbances which follow an arbitrary correlation process can easily be transformed into a white noise sequence. This is of special interest for Maximum Likelihood estimation. Attention is paid to the appropriate estimation method for the specific situation that observations are missing. Maximum Likelihood estimation of dynamic models is also considered. The final chapter is concerned with several test strategies for detecting the genuine correlation structure among the disturbances. The geometric approach throughout the book provides a coherent insight in apparently different subjects in the econometric field of time series analysis.

Release date Australia

May 7th, 1991

Author

Paul Knottnerus

Audiences

Postgraduate, Research & Scholarly
Professional & Vocational

Edition

Softcover reprint of the original 1st ed. 1991

Illustrations

1 Illustrations, black and white; VIII, 196 p. 1 illus.

Pages

196

Series

Lecture Notes in Economics and Mathematical Systems

Dimensions

170x244x11

ISBN-13

9783540539018

Product ID

14286235

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