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Elements of Forecasting
 
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Elements of Forecasting (Hardcover)

by Francis X. Diebold (Author)
1.0 out of 5 stars See all reviews (1 customer review)
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Product details

  • Hardcover: 384 pages
  • Publisher: South-Western, Div of Thomson Learning; 4th Revised edition edition (1 May 2006)
  • Language English
  • ISBN-10: 032432359X
  • ISBN-13: 978-0324323597
  • Product Dimensions: 24.1 x 19 x 1.8 cm
  • Average Customer Review: 1.0 out of 5 stars See all reviews (1 customer review)
  • Amazon.co.uk Sales Rank: 419,329 in Books (See Bestsellers in Books)

    Popular in these categories:

    #48 in  Books > Business, Finance & Law > Economics > Forecasting
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Product Description

Review
"The text is excellent from instructoras perspective. It is focused and comprehensive. The text is empirically oriented. It covers major issues of time-series econometrics at the undergraduate level. Including several comprehensive applications is a unique and outstanding feature of this book."

Product Description
Written by a leading expert on forecasting, this concise and modern text focuses on the core techniques of widest applicability and assumes only an elementary background in statistics. It is applications-oriented and illustrates all methods with detailed real-world applications, many of them international in flavor, designed to mimic typical forecasting situations. In many chapters, the application is the centerpiece of the presentation.

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17 of 17 people found the following review helpful:
1.0 out of 5 stars A sloppy book, 14 Jan 2004
By A Customer
This review is from: Elements of Forecasting (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. 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.

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