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Elements of Forecasting Hardcover – 1 May 2006


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Product details

  • Hardcover: 458 pages
  • Publisher: South-Western; 4th Revised edition edition (1 May 2006)
  • Language: English
  • ISBN-10: 032432359X
  • ISBN-13: 978-0324323597
  • Product Dimensions: 23.8 x 18.8 x 1.9 cm
  • Average Customer Review: 2.8 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon Bestsellers Rank: 683,233 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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1. Introduction to Forecasting: Applications, Methods, Books, Journals, and Software. Appendix: The Linear Regression Model. 2. Six Considerations Basic to Successful Forecasting. 3. Statistical Graphics for Forecasting. 4. Modeling and Forecasting Trend. 5. Modeling and Forecasting Seasonality. 6. Characterizing Cycles. 7. Modeling Cycles: MA, AR, and ARMA Models. 8. Forecasting Cycles. 9. Putting it All Together: A Forecasting Model with Trend, Seasonal, and Cyclical Components. 10. Forecasting with Regression Models. 11. Evaluating and Combining Forecasts. 12. Unit Roots, Stochastic Trends, ARIMA Forecasting Models, and Smoothing. 13. Volatility Measurement, Modeling and Forecasting.

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2.8 out of 5 stars
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Most Helpful Customer Reviews

24 of 24 people found the following review helpful By A Customer on 14 Jan. 2004
Format: Hardcover
There were a considerable number of errors in the first edition that I pointed out to the author shortly after its publication. The second edition seems to have corrected few if any of them. (page numbers refer to the second US edition.) In the US, the book is now in its third edition, and the mistakes STILL have not been corrected. Let me cite two egregious examples.
In the chapter on ARMA models, the example analyzed is Canadian Employment data. One of the models that is fit is an MA(4) -- see pages 164-6. When I tried to reproduce these results using software other than EVIEWS, using the data disk in the 1st edition, I couldn't. I contacted EVIEWS and they discovered a programming error in the estimation routine. They released a patch to fix EVIEWS. However, the author never re-estimated his model, and the estimates in the second edition are the same as in the first. However, my copy of the 2nd edition has no data disk! Was that thought to be an adequate solution?!
Chapter 9 ("Putting it all together") is a capstone chapter that analyzes liquor sales data using the techniques introduced in earlier chapters. After several pages (pp. 207-19) a model is selected. On pages 220-2, the residuals are examined using the Box-Ljung statistic, and deemed acceptable. However, as a careful examination of table 9.6 makes clear, the p-values for the Box-Ljung statistic were computed as if the input data were a raw series. The model generating the residuals (p. 219) had 3 autoregressive terms! This changes the d.f. in the chi-square distribution of the statistic. If you make the appropriate correction using the data in table 9.6, and compute the p-values correctly, you will see that the model residuals apparently ARE NOT white noise.
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2 of 2 people found the following review helpful By idic01 on 22 Nov. 2010
Format: Hardcover
This is the kind of book you read and you think "gosh I must be really slow, because I can't understand a thing"!

The sentences are long, the grammar is convoluted, the wording isn't simple, and there are many errors and sloppy ommissions as per other reviews.

Shame because this could be an excellent book with some proper editing, proof-reading and fixing the obvious mistakes. However it just drifts from one edition to another with none of this being done.
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Format: Hardcover Verified Purchase
Not the most fun book in the world but is useful to get an idea of forecasting. I would not recommend this to anyone who has not studied econometrics or at least stats in the past but if you have it is a useful starting point to learn from. The learning style is easy but a bit too wordy for me while the example data and questions are very useful to get a grip on the subject.
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By Lingbo on 10 Jan. 2013
Format: Hardcover Verified Purchase
I am pretty satisfied. It is a clean and new book. There is no sign inside the book.
Thank you very much!
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 11 reviews
44 of 50 people found the following review helpful
an embarrassingly slapdash and sloppy book 27 Sept. 2002
By Ronald Michener - Published on Amazon.com
Format: Hardcover
There were a considerable number of errors in the first edition that I pointed out to the author shortly after its publication. The second edition seems to have corrected few if any of them. Let me cite two egregious examples.
In the chapter on ARMA models, the example analyzed is Canadian Employment data. One of the models that is fit is an MA(4) -- see pages 164-6. When I tried to reproduce these results using software other than EVIEWS, using the data disk in the 1st edition, I couldn't. I contacted EVIEWS and they discovered a programming error in the estimation routine. They released a patch to fix EVIEWS. However, the author never re-estimated his model, and the estimates in the second edition are the same as in the first. However, my copy of the 2nd edition has no data disk! Was that thought to be an adequate solution?!
Chapter 9 ("Putting it all together") is a capstone chapter that analyzes liquor sales data using the techniques introduced in earlier chapters. After several pages (pp. 207-19) a model is selected. On pages 220-2, the residuals are examined using the Box-Ljung statistic, and deemed acceptable. However, as a careful examination of table 9.6 makes clear, the p-values for the Box-Ljung statistic were computed as if the input data were a raw series. The model generating the residuals (p. 219) had 3 autoregressive terms! This changes the d.f. in the chi-square distribution of the statistic. If you make the appropriate correction using the data in table 9.6, and compute the p-values correctly, you will see that the model residuals apparently ARE NOT white noise. One reason is a calendar effect in liquor sales: months that contain more than a usual number of Fridays and Saturdays result in more liquor sales; ones with more Sundays result in lower liquor sales. However, the author doesn't discover this, but accepts his inappropriate model on the basis of faulty distribution theory.
13 of 16 people found the following review helpful
Excellent introductory guide to forecasting !!! 26 Jan. 1999
By lawong@mbox3.singnet.com.sg - Published on Amazon.com
Format: Paperback
The use of practical examples (using the Eviews software) and the availability of a data disk makes this a very relevant guide for practitioners. There is a good section on graphical analysis and modelling of cycles using AR and MA processes. The mathematics is kept simple and clear, intuitive explanations are given throughout. The treatment of unit roots, cointegration and other advanced materials is quite sketchy but I guess that is to be expected in an introductory text. With the level of clarity evident throughout this book, I certainty hope Diebold follows up with another book on more advanced forecasting techniques.
2 of 2 people found the following review helpful
Not Bad 4 Jan. 2007
By Sebastian Krynski - Published on Amazon.com
Format: Hardcover
The book starts with talking about forecasting deterministic trends, then seasonalities, later chapters 6,7,8 talk about forecasting cycles. Finally in the end chapters the author puts it all together and talks about multivariable forecasting models. The book is on an introductory level, so if you're looking for indepth discussion of these topics this is not for you. Anoter drawback is that this book does not integrate into its discussion of the topics any examples of code that would show how to forecast with any popular software package (Eviews or SAS).
11 of 16 people found the following review helpful
Good, but poor examples 26 Nov. 1999
By John - Published on Amazon.com
Format: Paperback
If the purpose of using this book is to get a brief idea of what certain concepts are then it is a good book. Unfortunately, many people using this book are going to be those who do not have much background with the concepts inside and they will be looking for clearer explanations of what the author is talking about. I think that is the book's weakness: the fact that many times I didn't feel that his definitions and explanations were complete enough.
Very didactic 18 Feb. 2014
By jeydutra - Published on Amazon.com
Format: Hardcover Verified Purchase
I purchased the book for ECON 716 at The University of Kansas.
It is very didactic, very pedagogical , goes with you not only through the calculus, but also the reason behind them. Very good to get started into more serious time-series forecasting. However, too limited for advanced users. It serves its purpose as an introductory to intermediate book for time-series.
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