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
". . . the writing makes this book interesting to all levels of students. Bobko tackles tough issues in an easy way but provides references for more complex and complete treatment of the subject. . . . there is a familiarity and love of the material that radiates through the words."
--Malcolm James Ree, ORGANIZATIONAL RESEARCH METHODS, April 2002
"This book provides one of the clearest treatments of correlations and regression of any statistics book I have seen. . . . Bobko has achieved his objective of making the topics of correlation and regression accessible to students. . . . For someone looking for a very clearly written treatment of applied correlation and regression, this book would be an excellent choice."
--Paul E. Spector, University of South Florida
"As a quantitative methods instructor, I have reviewed and used many statistical textbooks. This textbook and approach is one of the very best when it comes to user-friendliness, approachability, clarity, and practical utility."
--Steven G. Rogelberg, Bowling Green State University
Building on the classical examples in the first edition, this updated edition provides students with an accessible textbook on statistical theories in correlation and regression. Taking an applied approach, the author uses concrete examples to help the student thoroughly understand how statistical techniques work and how to creatively apply them based on specific circumstances they face in the "real world."
The author uses a layered approach in each chapter, first offering the student an intuitive understanding of the problems or examples and progressing through to the underlying statistics. This layered approach and the applied examples provide students with the foundation and reasoning behind each technique, so they will be able to use their own judgement to effectively choose from the alternative data analytic options.
Customer Reviews:
The book was great.......2007-09-19
I received the book promptly and it was in excellent condition!Correlation and Regression: Principals and Applications for Industrial/Organizational Psychology and Management (Organizational Research Methods)
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Design for Six Sigma for Green Belts and Champions: Applications for Service Operations--Foundations, Tools, DMADV, Cases, and Certification (Six Sigma)
Howard S. Gitlow ,
David M. Levine , and
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Six Sigma for Green Belts and Champions: Foundations, DMAIC, Tools, Cases, and Certification
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The Six Sigma Handbook: The Complete Guide for Greenbelts, Blackbelts, and Managers at All Levels, Revised and Expanded Edition
ASIN: 0131855247 |
Book Description
Every major econometric method is illustrated by a persuasive, real life example applied to real data.
* Explores subjects such as sample design, which are critical to practical application econometrics.
Customer Reviews:
great!.......2007-09-04
It actually presents a good foundation in theory. It is completely unlike most undergrad statistics econometrics texts in that respect. Highly recommended!
Helpful text.......2006-03-13
I actually very much liked this textbook. The examples are both interesting and enlightening, and the explanations clear and easy to follow. As the title suggests, it is tailored to a course with a heavy math component, and the appendix, in particular, provides a good guide to equations, derivations, etc. I have also found it to be extremely useful in later econometrics applications, as its logical organization makes it very easy to find exactly the information you need.
good examples, complicated explanations, too many typos.......2004-02-26
As a student who never has taken econometrics class, I suffered the weakness of this book (which was used as the textbook) more than I benefited from the strengths.
The strength of this book lies in the good examples they use. The book is not dense and mostly the explanation is very
concise. The examples they have is very interesting and might attract students' attention quite well.
However, I found this book's explanation is not very biginner
friendly. The explanation is often unnecessary compliated with too much math, so it might not be good for introductory econometrics class (it could make a good supplemental reading).
Gujarati's, Kennedy, etc has similar coverage but the explanation is much more plain and reader friendly.
The crucial problem which makes this book less than desirable is, amount of typos - I'd say one typo in a few pages on average. Although they have listed erratas on the publisher's web-site, unfortunately they cover relatively low fraction of all the typos this book has (could be inevitable problem with the first edition book). End-of-chapter questions and slides suffer the similar problem, so when my professor did not catch the problem in the end-of-chapter question, I just wasted too much time trying to figure out the answers, which was the worst part of using this book.
Book Description
Both state-space models and Markov switching models have been highly productive paths for empirical research in macroeconomics and finance. This book presents recent advances in econometric methods that make feasible the estimation of models that have both features. One approach, in the classical framework, approximates the likelihood function; the other, in the Bayesian framework, uses Gibbs-sampling to simulate posterior distributions from data.
The authors present numerous applications of these approaches in detail: decomposition of time series into trend and cycle, a new index of coincident economic indicators, approaches to modeling monetary policy uncertainty, Friedman's "plucking" model of recessions, the detection of turning points in the business cycle and the question of whether booms and recessions are duration-dependent, state-space models with heteroskedastic disturbances, fads and crashes in financial markets, long-run real exchange rates, and mean reversion in asset returns.
Customer Reviews:
Amazing treatement for the practitioner and the student.......2007-02-06
This book gives a step-by-step treatement of models with regime changes and time varying coefficients. If you are a student or a practitioner you will find this book very useful to start your applications. The first six chapters are very well developed, and the GAUSS codes provided by the authors let you realize how to do the job. These chapters will let you estimate a model using the classical approach. However, the next chapters that cover exactly the same topics using a bayesian approach are not that well developed. The examples and explanations are not clear, and the few examples do not help you generalize the techniques to your own models. The first six chapters, however, make this book worth 5 stars!
State-Space Models with Regime-Switching:Classical and Gibbs.......2003-12-31
This is basically the only book around on this subject, and they do have useful informations as well. I think explanation is concise enough to clearly understand. I found this book to be useful because of those. The only thing is some typos (which I think is inevitable for this kind of book) and program software on the web which is not very clearly written.
A waste of time........2002-01-29
This book is poorly written. It has numerous typos. The authors never even bothered to explain some of the math notations they used. Apparently, I believe some the examples were copied from other books without a clear explanation of the notations. You'll end up scratching your head on the notations and typos. I wasted a lot of time reading this book. They never mentioned some of shorting comings of using Gibbs Sampling, and ignore some of alternative methods that are far superior in many other respects.
excellent book on regime switching.......2001-03-28
This is really great book for understanding regime switching and state-space models.As far as I know this is the first book that includes both topics together.It is easy to understand and supporting applications at the end of the each chapter make things easier for the reader.Furthermore, it also tells about bayesian econometrics and gibbs-sampling approach.In short,it is a must buy book for a economics graduate student who is interested in nonlinear time series econometrics
Book Description
In order to build a successful, Java-based application it is important to have a clear understanding of the principles underlying the various financial models. Those models guide the application designer in choosing the most appropriate Java data structures and implementation strategy. This book describes the principles of model building in financial engineering and explains those models as designs and working implementations for Java-based applications.
Throughout the book a series of packaged classes are developed to address a wide range of financial applications. Java methods are designed and implemented based on the most widely used models in financial engineering and investment practice. The classes and methods are explained and designed in a way which allows the financial engineer complete flexibility. The classes can be used as off-the-shelf working solutions or the innovative developer can re-arrange and modify methods to create new products
Customer Reviews:
"Let's code a bunch of financial formulas in Java".......2007-07-04
I wonder if anyone at Springer asked:
a) 'Why are we publishing a programming book?'
b) 'Why are we publishing a book by an unknown, untested author?'
c) 'Why hasn't anyone proof-read the darn thing?'
(c) is suggested by the very first paragraph, giving the plural of "student" as "student's"; those are eventually joined by "Black Schole's". Unexplained 'l-subscript-n' shows up in a formula, and turns out to be 'ln', a screaming typo repeated in each reference to logs.
The typos are accompanied by sloppy formulas and sloppy language, e.g., 'Stock price variations are binomial in a short time period'. Barker does not make a distinction between statistical and risk-neutral probability in that chapter, or discusses risk-neutral pricing elsewhere.
You get a laundry list of financial formulas, mostly related to exotic options (i.e. specific option-pricing models, simple enough to allow a closed-form solution), implemented in Java. I recall an option-formulas book listed in the references, and guess that the author went through it, culling the formulas and writing Java code, then did the same with a tome by Fabozzi ('Fabozi').
I don't care for a book on scalar arithmetics in Java. It's a far cry from the insightful, thoroughly-OOP, C++-based Joshi (2004).
Java Methods for Financial Engineering: Applications in Finance and Investment.......2007-06-26
Too bad there is nothing like this out there. I agree with comments above - methods and variables names are horrible. Plus, all the formatting seems to be gone. Very bulky code because of this.
CD would be nice to have. Although I personally do not need it. I have my own versions of almost all topics. Comparing the different implementations is what I was looking in this book. At the same typing is obnoxious...just for the record...
Would be nice to have Efficient Frontier chapter, portfolios' cookbook concepts and more on n-ary trees.
If you are looking for quant. finance books, unfortunately you do not have a lot of choice. This is a good book to have if you know what are you doing and need to solve little problems (my case). You have to know java and finance ( financial modeling and financial engineering) in order to read it.
/*******************************************/
All it really is just a one big financial calculator....
Best,
Tanya
Needs Support ( amongst other things).......2007-05-24
Not wanting to be pedantic but the naming of methods within the classes is pretty awful - for example on page 27 the author defines a method called 'retInitprice()'. Whaaat? I'm guessing its shorthand for 'returnInitialPrice'? But no, the method signature is 'public void retInitprice()'. Arrgh! So much for the concept of self-documenting code! Its hard enough reviewing someones code when it is well written so why on earth would you intentionally ( or otherwise ) give cryptic names to methods/classes/variables etc? The code is actually a distraction from the subject matter which is ironic given the subject matter. And yes, why isn't the code available via CD or download? Do I really need to sit and type this all in myself? God forbid. Come on Mr. Barker, get your publisher to do right by you and have them provide the source code via download so I can get on with analyzing what it is you've spent the last several months working on. We're all busy people, far too busy to sit and type needlessly!
Offering the benefit of the doubt with a warning until I am finished...........2007-05-23
There seem to be at least a few things about the book that might fill a gap and offer some combination of ideas not available or not as accessible elsewhere.
That said, a huge portion of the text is listings of source code (not entirely a bad thing) except that the code seems to be unavailable for download and there is no disk or CD. The listings for some of the core code are provided in a series of appendices which presumably you would have to type in to use.
Call me spoiled, but the type-it-in-yourself approach seems very 1970s to me.
I have written the author and publisher to see if the code is available.
In fairness to the author and in hopes that I will be pleasantly surprised, I will refrain from other comments until I have finished reading the book.
Book Description
Designed for undergraduate and MBA courses in regression analysis for business and economics, this text requires very little mathematical expertise beyond college algebra. Terry Dielman emphasizes the importance of understanding the assumptions of the regression model, knowing how to validate a selected model for these assumptions, knowing when and how regression might be useful in a business setting, and understanding and interpreting output from statistical packages and spreadsheets.
Customer Reviews:
Good for Excel and Minitab User.......2006-05-15
I checked Dr. Dielman's Applied Regression textbook out from Tsing-Hua Univ (Taiwan) Library for preparing my MBA thesis regarding the interaction effects. Given the widespread use of the SPSS or SAS, there are fewer texts writen in other commercially available packages. This book provided an alternative option for conducting the regression work.
The best book ever.......2001-05-24
This book takes you along the process of learning regression analysis easily. The author takes the time to show you how to solve real problems, the disc that comes with the book allows to even go deeper on the subject of study. The one who buys this book will never sell it, you'll need it all your professional life.
Average customer rating:
- Excellent explanations and examples
- An intuitive approach with good real world examples.
- Excellent book for use in and out of the classroom.
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Statistics for Business and Economics: Methods and Applications
Edwin Mansfield
Manufacturer: W. W. Norton & Company
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ASIN: 0393964604 |
Customer Reviews:
Excellent explanations and examples.......2007-08-03
I think Mansfield writes the most understandable textbooks available anywhere. This particular title was used in the MBA program at UF, and I have used it as a valuable desk reference ever since. I've also bought several copies through amazon (love the buy-it-used feature) and given them away to colleagues.
An intuitive approach with good real world examples........1999-03-25
An excellent review of the basic foundations of statistics with business applications. Does not contain the reams of computer output common in other texts. Nice real world examples. An intuitive approach with only moderate reliance on formulae. Good for classroom use or as a reference book for the business professional.
Excellent book for use in and out of the classroom........1999-02-02
An excellent survey of statistics. Prepares a student well for more specialized courses. Includes plenty of examples from economics, business and operations research. The textbook is also a great resource for the professional who needs to understand and apply statistics beyond an elementary level.
Book Description
What should you see when you’re analyzing real data using one of the major statistical packages, such as SPSS, SAS or Microsoft Excel? This book will show you, and will walk you through the output from a variety of statistical outcomes, such as data reflecting a single common factor. Through the use of actual demonstrations, the authors supply readers with the computer programs necessary to simulate data sets with the statistical properties (usually multivariate) that are often assumed of real data. The reader is then shown how to analyze these data sets and how to interpret the results. The book begins with a general introduction to doing research and tips for using the three statistical packages. The authors next explore how to create data structures and perform univariate, bivariate, and multivariate simulations. They then show how to use the simulations to understand common statistical algorithms and their outputs when doing a basic correlation analysis, exploratory factor analysis, confirmatory factor analysis, multidimensional scaling, multiple regression, discriminate analysis, classification analysis and MANOVA. Throughout the book, the authors provide the reader with helpful guides, such as: *Hint boxes to give readers tips for executing particular techniques using the statistical software packages. *Steps that show each stage of a procedure, such as importing an Excel file into SAS. *Problems end each chapter so the reader can practice the techniques described. *Web Site with the SAS and SPSS programs and sample data.
Average customer rating:
|
Large Sample Methods in Statistics: An Introduction with Applications
Pranab K. Sen , and
Julio M. Singer
Manufacturer: Chapman & Hall/CRC
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Binding: Hardcover
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ASIN: 0412042215 |
Book Description
This text bridges the gap between sound theoretcial developments and practical, fruitful methodology by providing solid justification for standard symptotic statistical methods. It contains a unified survey of standard large sample theory and provides access to more complex statistical models that arise in diverse practical applications.
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