- Paperback: 392 pages
- Publisher: Cengage Learning; 1 edition (September 2, 1997)
- Language: English
- ISBN-10: 0538862440
- ISBN-13: 978-0538862448
- Product Dimensions: 0.8 x 7.5 x 9.5 inches
- Shipping Weight: 1.8 pounds
- Average Customer Review: 15 customer reviews
- Amazon Best Sellers Rank: #1,579,190 in Books (See Top 100 in Books)
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Elements of Forecasting 1st Edition
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"The text is excellent from instructor's 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."
"I will adopt the new edition. Coverage and organization of the book are excellent and focused on the student while giving many pointers and references to advanced material and even current research."
"The strength of the Diebold text is that it covers sufficiently diverse topics related to forecasting methods (compared with other books in the market). Also, its nicely organized flow of the topics should be very accessible to many readers, which is the primary reason why I assigned this book to my students." --This text refers to the Hardcover edition.
About the Author
Francis X. Diebold is William Polk Carey Professor of Economics, and Professor of Finance and Statistics, at the University of Pennsylvania and its Wharton School, and Faculty Research Associate at the National Bureau of Economic Research in Cambridge, Mass. He is a leader in forecasting, econometrics, risk management, quantitative finance, and macroeconomics, with extensive experience simultaneously in academic, corporate, and policy circles. Dr. Diebold has published more than one hundred articles and ten books and edited volumes. He has received widespread recognition for his work, including election to Fellowship in the Econometric Society, Sloan and Guggenheim Fellowships, and election to advisory and editorial boards of numerous leading journals, including Econometrica and Review of Economics and Statistics. Dr. Diebold is equally active in corporate and policy affairs, and he is consulted regularly by financial firms, governments and multilateral organizations, worldwide. His latest book is Measuring and Forecasting Financial Market Volatilities and Correlations. Dr. Diebold is a popular lecturer, both in the U.S. and internationally. He has held visiting appointments in Economics and Finance at Princeton University, Cambridge University, the University of Chicago, the London School of Economics, and New York University. He is also active in executive education; his ongoing annual courses include those at the International Monetary Fund (Washington, DC) and FAME (Geneva). He has received several prizes for outstanding teaching. Dr. Diebold received his B.S. from the Wharton School in 1981 and his Ph.D. in 1986. Before returning to the University of Pennsylvania in 1989, he worked as an economist under Paul Volcker and Alan Greenspan at the Board of Governors of the Federal Reserve System in Washington DC. He is married with three children and lives in Wayne, Pennsylvania.
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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.
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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