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Forecasting with Dynamic Regression Models, by Alan Pankratz
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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.
- Sales Rank: #401581 in Books
- Published on: 1991-10-10
- Original language: English
- Number of items: 1
- Dimensions: 9.69" h x .97" w x 6.42" l, 1.66 pounds
- Binding: Hardcover
- 400 pages
From the Publisher
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.
Most helpful customer reviews
8 of 8 people found the following review helpful.
Time Series Analysis for beginner
By A Customer
It is an excellent book for understanding time series analysis, well written and clearly explained. Readers should have some knowledge of univariate ARIMA model but no expertise is required. The book focuses on how, why, and when to combine the effect of other variables and the univariate ARIMA model. The suggested methods are demonstrated by a well-explained example. The author describes each step in the analysis in detail, this greatly helps in understanding the logic. Therefore, I highly recommend this book for students or persons who have a preliminary knowledge of the time series analysis technique, and who want to develop a more in-depth understanding of the subject. In addition, it is a must-have reference book for all time-series analyst.
2 of 2 people found the following review helpful.
Start with this book first!
By reader
I've ordered over 10 time series books and avoided buying this one because of the price -- I was wrong! I should've purchased this one first and the others later. The author presents the motivation of the theory and simplifies it for the beginning forecaster. His examples are helpful and fully clear. Interestingly, he presents the material in the fashion of a spy novel, giving you enough information so that you can't wait to read the next section. While there have been many advances since he wrote this book, his book lays a solid foundation for understanding more fully what statistical techniques and tests were to follow. I also appreciated the development in the complexity of the formulas -- I was able to follow them with full understanding to the end. I only wish that he had written a text that incorporated new advances. Quick start your learning with this text -- then move to others afterwards!
1 of 1 people found the following review helpful.
tharit
By T. Issara-yangyun
I was assigned to develop a stochastic model for air pollution (well, back to 3 weeks before, I had no idea what time series analysis was !!!). After spend some time searching around for an easy-to-understand textbook dealing with time series data, I found this book is the best. It is comprehensive and clearly explain with many good excercises. What you need is some basic knowledge in ARIMA (which could be find from general econometrics textbook or SPSS guideline) and simple linear regression and then you will love this book as I am now.
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