Forecasting with Dynamic Regression Models (Hardcover)

Alan Pankratz

ISBN 10: 0471615285 / ISBN 13: 9780471615286

Published by John Wiley & Sons Inc, New York, 1991

About this Item

Hardcover. One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies. This study presents the concepts and practice of interpreting single equation dynamic regression models. Emphasis is placed on possible dynamic patterns, such as distributed lag responses of the output series to the input series and the auto-correlation patterns of the regression disturbance. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9780471615286

Bibliographic Details

• Title: Forecasting with Dynamic Regression Models (Hardcover)
• Publisher: John Wiley & Sons Inc, New York
• Publication Date: 1991
• Language: English
• ISBN 10: 0471615285
• ISBN 13: 9780471615286
• Binding: Hardcover
• Edition: 1st Edition
• Condition: new

About this title

Synopsis

One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.

About the Author

Alan Pankratz is the author of Forecasting with Dynamic Regression Models, published by Wiley.