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.
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
David Hendry is one of the world's leading econometricians, and in this major new work he and Michael Clements provide an extended formal analysis of economic forecasting with econometric models: their analysis builds in many of the features of the real world that are often overlooked in traditional, textbook analyses of forecasting. Consequently, Clements and Hendry are able to suggest ways in which existing forecasting practices can be improved, as well as providing a rationale for some of the habitual practices of forecasters that have hitherto lacked a scientific foundation.
Customer Reviews:
Good strategies for macro economic forecasting.......2000-03-28
This is a unique text that treats economic time series forecasting with emphasis on the recent advances in econometric theory such as cointegration as well as other practical strategies such as combination forecasts. Usual text books do not have the breadth of coverage this one attempts, successfully, to achieve. In short, this one text replaces many books and papers on one's shelf.
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.
Product Description
An enlightening and educational book that succinctly communicates the practical aspects of forecasting. It is written with practicing forecasters in mind, yet is simple and easy to understand so that a person with little or no background in statistics can follow.
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.
Average customer rating:
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Neural Network Time Series: Forecasting of Financial Markets
E. Michael Azoff
Manufacturer: John Wiley & Sons
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ASIN: 0471943568 |
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Better than I expected.......1997-02-26
A good introduction to neural nets and their applicability tothe futures markets. Provides mathematical/theoretical basis fortechniques involved in neural net training, testing, chaos and touches on nonlinear systems in general. Also provides interesting benchmarks of several nets against several time series. Great bibliography. Includes Fortran source code for running trained nets but not for training a net.
Average customer rating:
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Quantitative Forecasting Methods (Duxbury Series in Statistics and Decision Sciences)
Nicholas R. Farnum , and
Laverne W. Staton
Manufacturer: Wadsworth Publishing Company
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ASIN: 0534916864 |
Book Description
Now you can slash manufacturing and inventory costs while improving customer service through just-in-time forecasting and scheduling. Manufacturing managers and supervisors will discover proven skill-building exercises and five major forecasting tools to help them keep their businesses competitive.
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