Book Description
This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied in economics, engineering, and the natural and social sciences. The book assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This second edition contains detailed instructions on the use of the new totally windows-based computer package ITSM2000, the student version of which is included with the text. Expanded treatments are also given of several topics treated only briefly in the first edition. These include regression with time series errors, which plays an important role in forecasting and inference, and ARCH and GARCH models, which are widely used for the modeling of financial time series. These models can be fitted using the new version of ITSM. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include the Burg and Hannan-Rissanen algorithms, unit roots, the EM algorithm, structural models, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introductions are also given to cointegration and to non-linear, continuous-time and long-memory models.
Customer Reviews:
good basic intro.......2006-11-10
A decent basic introduction covering a lot of topics. It's much more accessible for learning the subject for the first time then many other books which pile on the mathematical notation and obscure the actual meaning of things. The accompanying CD is very nice, although it gets annoying very fast that you're restricted to very small dataset sizes---but it does help in learning. The only two things that are somewhat of a problem with this book are 1) many times, rather than clearly stating "here's the algorithm you need to implement", you are referred to 3 or 4 other sections of the book for pieces of the algorithm, often without a clear explanation of exactly how that earlier section is supposed to be worked into the current desired algorithm and 2) there aren't a lot of practical insights as to how to actually initialize many of the algorithms (everything is great if you already know all the parameters in advance but starting from scratch with just raw data isn't dealt with I think as fully as would be useful). All in all, though, the book is helpful and, as I said, very good for learning the essential concepts for the first time.
When is an Introduction not an Introduction?.......2006-11-05
In the process of building a website targeted to those good folks that are striving valiantly to make a living through Internet marketing, you might think that an early objective would be to assemble a library of good reference material. After all, if you are planning on providing sensible information to your readers, then you should have a few good text books on hand to refer to when you need to be sure that some little tidbit of information might actually work. Well, at least I did. So, I have been scouring the Internet for textbook on the subject of Forecasting, which we share a common interest in. I have purchased a few and, for the most part, they are really quite informative and will be useful when the time comes. There is, however, an exception to this.
One book I purchased bears the title "Introduction to Time Series and Forecasting, Brockwell, Peter J and Richard A Davis". Being an intelligent sort of chap, I naturally took the word "Introduction" to mean just that. You know, you've been introduced to people before and becoming introduced usually means that 1. You look at the face. 2. You grasp their hand and shake firmly and 3. You exchange pleasantries, such as "Hello, it's nice to meet you".
Now, I never blame the person making the introduction if the relationship doesn't work out. After all, it's not their fault that two people hopefully sharing a common interest (after all, why bother making an introduction?) aren't all that compatible. There are likely to be many reasons for the incompatibility, the first of which could be that people travel in different circles and your circle isn't ever going to be part of their circle. Sort of an exclusionary relationship, you might say. And, not to be overly judgmental of others, of course, there may be plenty of good reasons for that. If everyone existed in one social circle, after all, the world would be beyond boring.
Anyways, the text book is a wonderful creation, that is, if you're a post-graduate or doctoral candidate. Upon opening the cover, expecting to be warmly introduced, I was rather amazed at the depth of equations and formulas gracing practically every page. I felt intimidated immediately. Remember the movie "The Ring"? This had to be rocket science, or more correctly, forecasting science at its most extreme! Wow! I should have really paid more attention during my statistics classes. So, I quickly closed the cover and tried to get a refund from the seller. Note the word Tried here. They didn't want it back either.
The good Post-Grand and PhD. candidates of the science of forecasting probably don't need an "Introduction" to Time Series and Forecasting. Next time I buy a book, I think I'll look for something with "Sandbox" in the title.
May all your Forecasts be Good Forecasts at [...]
Awesome.......2006-08-04
this book is excellent because it provides us with many examples and detailed explanations.
Not sure if it is introductory.......2005-12-24
I think the book is not written in a very organized way. It's not a book for picking up time series quickly. It's saturated with information, which I'm not sure if it's necessary for implementation. I have no problem following the math, however, if I want to pick up something and implement it within a day or two, the book is a bit harder to digest. Wouldn't think this is an undergraduate course book as it covers convergence in probability or mean-squared, which I learnt in PhD courses, not even master level.
Great book for a great price.......2004-02-12
This is one of those books that you can't find much cons to it. The book is inexpensive, and it's unbelievably lightweight. The material is rich, and yet easy to understand. The author actually brings you step by step from elementary to theorectical proofs.
Book Description
ELEMENTARY FORECASTING focuses on the core techniques of widest applicability. The author illustrates all methods with detailed real-world applications, many of them international in flavor, designed to mimic typical forecasting situations.
Customer Reviews:
Not Bad.......2007-01-04
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).
Third edition is no better.......2004-01-15
I posted the unfavorable review of the second edition. I have recently had an opportunity to see the third edition, and find the same errors are still present.
an embarrassingly slapdash and sloppy book.......2002-09-28
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.
Good, but poor examples.......1999-11-27
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.
Excellent introductory guide to forecasting !!!.......1999-01-26
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.
Book Description
This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated, vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood, and Bayesian methods are considered for estimating these models. Different procedures for model selection and model specification are treated and a wide range of tests and criteria for model checking are introduced. Causality analysis, impulse response analysis and innovation accounting are presented as tools for structural analysis.
The book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their tasks. It bridges the gap to the difficult technical literature on the topic.
Customer Reviews:
Welcomed Surprise.......2007-10-13
This book provides a fairly elementary view of the vast subject of time series analysis. It easy to read and the author provides lots of basic calculations. Typically, such books stay away from the cutting edge topics but not this one. It is quite complete. I highly recommend it to anyone that knows a few basic things about time series and wants to take it much further.
Book 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.
Book Description
Economies evolve and are subject to sudden shifts precipitated by legislative changes, economic policy, major discoveries, and political turmoil. Macroeconometric models are a very imperfect tool for forecasting this highly complicated and changing process. Ignoring these factors leads to a wide discrepancy between theory and practice.
In their second book on economic forecasting, Michael P. Clements and David F. Hendry ask why some practices seem to work empirically despite a lack of formal support from theory. After reviewing the conventional approach to economic forecasting, they look at the implications for causal modeling, present a taxonomy of forecast errors, and delineate the sources of forecast failure. They show that forecast-period shifts in deterministic factors--interacting with model misspecification, collinearity, and inconsistent estimation--are the dominant source of systematic failure. They then consider various approaches for avoiding systematic forecasting errors, including intercept corrections, differencing, co-breaking, and modeling regime shifts; they emphasize the distinction between equilibrium correction (based on cointegration) and error correction (automatically offsetting past errors). Finally, they present three applications to test the implications of their framework. Their results on forecasting have wider implications for the conduct of empirical econometric research, model formulation, the testing of economic hypotheses, and model-based policy analyses.
Customer Reviews:
Excellent.......2000-04-30
The book is up-to-date and advanced where materials cannot be found from some other general time series texts.
Book Description
This book covers time series modeling and forecasting for econometrics and finance students. This new edition has been simplified for more ease of use and includes new chapters and substantial important revisions.
Customer Reviews:
Main Focus on cointegration + use of PcGive & G@RCH.......2004-11-30
This book is mainly focused on cointegration(the title is quite misleading). The treatment is nice and NOT superficial. Every chapter deals (in)directly with cointegration, except the last one on GARCH models. There is even a chapter on panel data models and cointegration. Do NOT buy the book to get an extensive treatment of "time series modelling and forecasting" techniques. This book is useful if you need to apply cointegration in your work. This book should not be given to students as an introductory book on "time-series modelling and forecasting". Chris Brooks' Introductory Econometrics for Finance is much better for that purpose. Finally, a nice feature of the book is the use of PcGive and G@ARCH Ox packages.
Information on one of the two authors.
http://www.dur.ac.uk/robert.sollis/
Supplementary materials.
http://www.wiley.co.uk/harris/supp.html
Petition: info about authors.......2003-06-19
Please provide brief bios of these two authors.
Book Description
Time Series Models for Business and Economic Forecasting is the most up-to-date and accessible guide to one of the fastest growing areas in business and economic analysis. The author is regarded as one of the most accomplished econometricians in Europe and this book is based on his highly successful lecture program for multidisciplinary, graduate and upper level undergraduate students. Early chapters of the book focus on the typical features of time series data in business and economics. Later chapters are concerned with the discussion of some important concepts in time series analysis, the techniques that can be readily applied in practice, different modeling methods and model structures, multivariate time, and the common aspects across time series.
Customer Reviews:
Good introductory book !.......2003-02-03
Full of real-life examples that provide some intuitive insight about the issues that may arise when modelling time series and forecasting. Requires some initial knowledge in statistics and algebra but if you're involved in time series modelling, it should be your first book. All the data thats used is available in the authors webbsite for downloading, very nice.
nice book on time series for statisticians and economists.......2001-07-01
To make this review short, I will say that I agree with all seven points made by the reviewer from New York, NY, whomever he or she may be. Franses is clear, concise, authoritative and up-to-date on all the advances.
I particularly like the nice coverage of GARCH models that are new to me. It is a great introductory text especially for economics majors. For more advanced books and other treatments of time series consider Kennedy's fourth edition of "A Guide to Econometrics" or the suggestion from reviewer "New York, NY". Also my listmania list on time series will give you several sources to look at.
Excellent introductory book on economic time series modeling.......2000-04-10
Recently, I reread Franses book and expanded my review, which now includes 10 benefits.
(1) Organization by key features of economic time series (trends, seasonality, outliers, conditional heteroskedasticity, non-linearity), rather than by methods, which provides a practical foundation for the various methodologies. The order in which chapters are presented reflects the order of difficulty in modeling trends, seasonality, etc. Even if there were no other benefits, this organization makes it worthwhile.
(2) Appropriate level for first book on time series models as applied to economic time series, explaining more difficult concepts GARCH and VAR without excess detail. Box and Jenksins book is more a textbook; Brockwell and Davis is also more advanced; Hamilton is comprehensive and technical, but not as friendly. This book is very approachable even if you have had only 1 or 2 statistics courses. In economics, many people are interested in forecasting, and Franeses here is a good start. If you are looking for a more advanced forecasting book, try the recent books by Clements and Hendry from Cambridge U Press.
(3) Clear distinction of the steps of model identification, estimation, diagnostics, and selection; something which other time series analysis books do not seem to do early or easily. (4) Delineates stochastic and deterministic models in the second chapter, providing a framework for when to take differences (eg. ARMA vs ARIMA). His timing is excellent. Many people I have interviewed on time series do not understand why they need to difference (eg use prices instead of returns) or why to transform the series (eg use logs instead of actual values).
(5) Generous use of examples with real not simulated data with a website to download all the data, making it possible to import, graph, and analyze on your own.
(6) A website containing printing corrections. Techincal books are likely to have some errors, but very few keep websites to list what those are.
(7) Revealing graphics, especially for conditional heteroskedasticity, the 'CH' in GARCH. Figures 7.1-7.3 illustrate the concept that large returns tend to follow large returns very cleanly.
(8) His notation is clear and consistent, yet not overwhelming: conventional Greek letters, only 1 level of subscripting, matrix noation where appropriate; even the results are neatly presented, as standard errors appear in () below their point estimates. Finally, Franses uses the same notation from chapter to chapter where the term is the same--not so common when chapters written by different authors.
(9) Great appendices: extensive and updated references, a thorough subject index, and an author index. My only suggestion for improvement is that a second edition or the website should contain some exercises. Highly recommended.
(10) The price! There are books published under Wiley at 3 to 4 times the price! under Springer Verlag for 2 to 3 times the price. Certain books are worth the money, but Cambridge University Press paperback publications, when written well, are exeptional values. I encourage the ambitious time series student to look at other time series books, including one written this year by Franses including Quantitative Models in Market Research.
Excellent introduction into time series.......2000-04-10
This book is a brilliant introduction into time series analysis. I found it a great basis for further analysis, allowing to go into deep with, for example, J.D. Hamilton's classical work. The book has a very well-defined structure, which (in my opinion) serves both auto-didact and (under)graduate teaching. Check out the author's web-page at Erasmus University Rotterdam for a list with corrections of some typos and the data sets used.
This book is exceptional.......1999-11-21
The beauty of this text is it's clarity and the author's choice to stay away from didactic lectures on formal statistical mathematics. I would highly recommend this book for anyone who has an undergraduate background in mathematics, statistics or economics and wants a medium level text to show them how to model time series.
Average customer rating:
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Handbook of Applied Econometrics and Statistical Inference (STATISTICS: TEXTBOOKS AND MONOGRAPHS)
Aman Ullah
Manufacturer: CRC
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Book Description
Summarizing developments and techniques in the field, this reference covers sample surveys, nonparametric analysis, hypothesis testing, time series analysis, Bayesian inference, and distribution theory for applications in statistics, economics, medicine, biology, engineering, sociology, psychology, and information technology. It supplies a geometric proof of an extended Gauss-Markov theorem, approaches for the design and implementation of sample surveys, advances in the theory of Neyman's smooth test, and methods for pre-test and biased estimation. It includes discussions ofsample size requirements for estimation in SUR models, innovative developments in nonparametric models, and more.
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Business Cycles
Francis X. Diebold , and
Glenn D. Rudebusch
Manufacturer: Princeton University Press
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ASIN: 0691012180 |
Book Description
This is the most sophisticated and up-to-date econometric analysis of business cycles now available. Francis Diebold and Glenn Rudebusch have long been acknowledged as leading experts on business cycles. And here they present a highly integrative collection of their most important essays on the subject, along with a detailed introduction that draws together the book's principal themes and findings.
Diebold and Rudebusch use the latest quantitative methods to address five principal questions about the measurement, modeling, and forecasting of business cycles. They ask whether business cycles have become more moderate in the postwar period, concluding that recessions have, in fact, been shorter and shallower. They consider whether economic expansions and contractions tend to die of "old age." Contrary to popular wisdom, they find little evidence that expansions become more fragile the longer they last, although they do find that contractions are increasingly likely to end as they age. The authors discuss the defining characteristics of business cycles, focusing on how economic variables move together and on the timing of the slow alternation between expansions and contractions. They explore the difficulties of distinguishing between long-term trends in the economy and cyclical fluctuations. And they examine how business cycles can be forecast, looking in particular at how to predict turning points in cycles, rather than merely the level of future economic activity. They show here that the index of leading economic indicators is a poor predictor of future economic activity, and consider what we can learn from other indicators, such as financial variables. Throughout, the authors make use of a variety of advanced econometric techniques, including nonparametric analysis, fractional integration, and regime-switching models. Business Cycles is crucial reading for policymakers, bankers, and business executives.
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Introduction to Time-Series Modeling and Forecasting in Business and Economics
Patricia E. Gaynor , and
Rickey C. Kirkpatrick
Manufacturer: McGraw-Hill Companies
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ASIN: 0070349134 |
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