Forecasting, Structural Time Series Models and the Kalman Filter
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Average customer review:Product Description
This book provides a synthesis of concepts and materials that ordinarily appear separately in time series and econometrics literature, presenting a comprehensive review of both theoretical and applied concepts. Perhaps the most novel feature of the book is its use of Kalman filtering together with econometric and time series methodology. From a technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. This technique was originally developed in control engineering but is becoming increasingly important in economics and operations research. The book is primarily concerned with modeling economic and social time series and with addressing the special problems that the treatment of such series pose.
Product Details
- Amazon Sales Rank: #562065 in Books
- Published on: 1991-04-26
- Number of items: 1
- Binding: Paperback
- 570 pages
Editorial Reviews
Review
"A well-written book by an author who has made numerous important contributions to the literature of forecasting, time series, and Kalman filters. It is a practical book in the sense that it not only discusses the definitions, interpretations, and analyses of structural time series models, but also illustrates the techniques." Choice
"It is difficult to compare this well-written, practical book to other books on time series because it is unique and unconventional in its approach to the subject....It accomplishes the difficult task of making the subject accessible to students and practitioners having relatively modest preparation in mathematics and statistics. I recommend it for acquisition by any undergraduate/graduate sciences or mathematics library, and it would be an excellent choice for a wide variety of classroom uses." John E. Angus, Technometrics
Customer Reviews
Excellent for reference and insights as well.
Harvey's book is an excellent text on treatment of forecasting and structural time series models. Although I would say this book is really a text reference, he does add insights intewoven throughout the book.




