Book Description
APPLIED REGRESSION ANALYSIS applies regression to real data and examples while employing commercial statistical and spreadsheet software. Covering the core regression topics as well as optional topics including ANOVA, Time Series Forecasting, and Discriminant Analysis, the text 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:
Great Minitab Resource.......2007-05-15
Dr. Dielman was a professor of mine. The book is well organized and useful for all people on all levels.
Excellent.......2006-12-26
For my class, the professor assigned Kleinbaum et al's textbook rather than this one. As I mentioned in my review for that book, it was so confusing and poorly organized. Luckily, I found this book in the library and used it instead and ended up a lot less confused in class than my classmates who were trying to understand the Kleinbaum book. Dielman's book is very well organized and laid out. It doesn't have colorful bells and whistles since it's a fairly upper level book, but it does have a very user-friendly layout. Furthermore, the formulas are never presented without an accompanying explanation in plain English and examples of how and when to use them. Another thing I really like about this book is that it gives thorough directions on how to do a lot of the analyses on some common statistical packages. Many of the instructions are accompanied by screenshots. They're at the end of each chapter rather than interspersed in the text, which makes them easy to find. This is actually becoming a great SAS manual for me.
For anyone struggling with the Kleinbaum book, or for any instructor considering using the Kleinbaum book, I would highly recommend this one instead.
A Cross-platform textbook.......2006-05-15
I have bought Dr. Dielman's Applied Regression 4e textbook in Taiwan) for preparing my MBA thesis regarding the interaction effects. Given the widespread use of commercially available packages, this book provided considerately coverage on ALL computer packages about the field of regression work.
Book Description
This book systematically and thoroughly covers a vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the past five decades. Within this framework, this is the first book to discuss the principles of the nonparametric approach to the topics covered in a first year graduate course in econometrics, e.g., regression function, heteroskedasticity, simultaneous equations models, logit-probit and censored models. Professors Pagan and Ullah provide intuitive explanations of difficult concepts, heuristic developments of theory, and empirical examples emphasizing the usefulness of modern nonparametric approach. The book should provide a new perspective on teaching and research in applied subjects in general and econometrics and statistics in particular.
Customer Reviews:
Decent introduction, not really mathematically elegant.......2007-10-10
I was compelled to buy this book because I am a practitioner who needed to know what it means to use a kernel regression to separate the trend from the cycle in macroeconomic statistics. I have been out of graduate school for a while now, and I am not up to speed on non-parametric techniques in econometrics.
The authors are adept at introducing the subject, and they use the natural logical progression: first they show how to estimate a density; and then they lead the reader to think of a kernel regression as the conditional moment of a bivariate density.
The author's notation confused me because to estimate a density is really to estimate a function at each of the points in its domain. Sometimes the authors made this point clear and sometimes their arguments obfuscated this subtlety.
A strength seems to be the breadth of the bibliographic references. Another strength seems to be the empirical applications that follow the cursory theoretical discussions.
The book will appeal to a middle-brow reader like myself, but mathematicians and good statisticians may find the analysis a trifle loose.
The best introduction to the field.......2005-01-31
I think this is the best introduction to nonparametric and semiparametric estimation to date. It covers an impressive amount of material, and the focus is on density and regression estimation. The exposition is clear, a lot of crucial results are proved, and an immense quantity of others are sketched or at least mentioned. Most of the book is fairly or very advanced, but all topics are introduced in a neat, simple, and intuitive way, so even a beginner can benefit from several parts of this book (previous good knowledge of math & statistics are still necessary, of course). Densities, regressions, discrete dependent variable models, simultaneous equation models, selection models, it's all in here. The field is expanding, but this book really has almost all you need to know about what the field has done until the publication date.
There is also a nice and useful appendix for many of the asymptotic results used in the book. The only drawback (besides a few typos, but not so many to be annoying) is the scant presence of empirical applications, but this book is not supposed to be a guide for applied econometricians (at least, not mainly), so I don't think it's a serious shortcoming. If you are more interested in the applied side of np regression, but you still want a rigorous treatment, you may look at Yatchew's "Semiparametric regression for the applied econometrician", in the same Cambridge series. For an even simpler, shorter, and low-tech introduction to np esimation of densities and regression, I would suggest instead the last pages of Ch. 3 in the splendid "The analysis of household surveys", by Angus Deaton.
This book is really worth its price (which, by the way, is kept at a very decent level by the worthy Cambridge University Press. I wish Wiley or Chapman and Hall stopped with their policy of immoral prices...). Highly recommended.
Great Book on Non-Parametrics.......2001-10-20
I just started reading it, and I love the clear exposition of the book. Its a very fast-growing field, so don't expect this book to be the last word on the subject. Still, it's a must for an advanced graduate student in econometrics in need of a good introduction to non-parametric estimation.
A comprehensive review of nonparametrics statistics.......2001-04-08
Nonparametrics seems to be one of the most promising fields in econometrics. All econometricians should be aware of that and try to learn the basic tools. This book is a great beginning (perhaps you should read the chapter of nonparametrics in Johnston and Dinardo's "Econometric Methods" to get used to the very basic concepts). The manual contains practically all the stuff that has been done in the field. It begins pretty fast with the kernel estimation method and, by page 19, you will be face to nonparametric derivatives estimation equations. In the introduction there is a clear explanation of the difference between parametrics and nonparametrics; you will also learn the main basic methods and concepts, such as the nearest Neighborhood Estimator and the window's size problem. After that, you'll have to read about the statistical properties (finite sample and asymptotics) of the estimators. There is also a lot of stuff of semiparametric methods. You shouldn't expect an extremely easy-to-read manual, because nonparametrics is a pretty complex subject. The first 50 pages are easy and fun to read. You'll get excited by learning such interesting theory. But then, the hard topics begin and if you want to understand them all, you'll have to make a big effort. Not overwhelmingly complicated, neither elementary, this book is an excellent reference in the field, but I advice you to have two or three more books of the same subject (Hardle, for example) so you can understand faster some of the developments presented. A fairly good mathematical and probability knowledge is required.
Up to date.......2001-02-03
This is the most accessible and the most comprehensive text on nonparametric econometric methods I have seen. The field is highly technical, and there has been a need for the book that would combine ease-of-use with the scope. Moreover, the book is up to date and covers all econometric methods, instead of focusing on a specific branch. Recommended.
Book Description
- The updated and expanded second edition of the internationally bestselling guide to principles and practices for undergraduate business and economics students taking mandatory economics statistics courses. - Features four new sections—on nonparametric tests, the Logit Model, the Probit Model, and causality tests—complete with new models and tests used in financial econometrics, and a new chapter on time series econometrics - Over 100,000 students enrolled annually - Includes numerous examples, completely worked problems, supplementary problems, and two full-length self-examinations
Customer Reviews:
It got me through Econometrics.......2002-01-26
This was an extremely useful book for the understanding of Statistics and Econometrics. Each topic had examples to show how the formulas work. The computer chapter went over the programming in SAS, Excel, and Eviews for the problems in the book. Best of all, the problems had answers. This is a must-have for beginning statistics and econometrics since it starts from scratch, and for theory students in search of an application.
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
To harness the full power of computer technology, economists need to use a broad range of mathematical techniques. In this book, Kenneth Judd presents techniques from the numerical analysis and applied mathematics literatures and shows how to use them in economic analyses.
The book is divided into five parts. Part I provides a general introduction. Part II presents basics from numerical analysis on R^n,including linear equations, iterative methods, optimization, nonlinear equations, approximation methods, numerical integration and differentiation, and Monte Carlo methods. Part III covers methods for dynamic problems, including finite difference methods, projection methods, and numerical dynamic programming. Part IV covers perturbation and asymptotic solution methods. Finally, Part V covers applications to dynamic equilibrium analysis, including solution methods for perfect foresight models and rational expectation models. A web site contains supplementary material including programs and answers to exercises.
Customer Reviews:
Great job!.......2007-05-19
The book was in a great condition, and it arrived just as promised.
An excellent and useful text.......2006-09-10
This is the type of book I've been looking for for a long time: It tells you directly what problems are solved by numerical approximation, what methods have been developed for such applications, how to use them, what to watch out for and most importantly, what "tricks" are available to make things easier - this is something you will never pick up in an academic paper and in very few courses.
The structure is very illuminating: simple examples of common problems are followed by generalized versions which are usefull for anyone to apply to their own work. Care is taken to point out the strenghts and weaknesses of various procedures so that the best one can be selected.
As to the critisisms that it does not go deep enough: its not supposed to. It covers in enough detail most (all) of the important methods used by the top economic researchers today, and if the problem you are working on requires more detail than is in the text, precise and extensive references are provided to further understand that particular area.
a very practical and forthright book.
too hard for uninitiated.......2006-07-28
this book may be good for those who already know smth about numerical methods.
An essential resource for all applied economists........1999-05-04
Judd ties together a vast amount of material--from the most basic to the most advanced--that is essential to anyone doing computational work in economics, econometrics or finance. The book is sufficiently self-contained to serve as the single reference book on computational methods for the average economist. In addition, Judd highlights the origins of most methods and points to strengths, weaknesses, and future theoretical research directions. Economic/finance examples are used throughout the book to make the concepts easy to understand and apply. The only thing keeping this book from being perfect is a complete set of software tools, but given the breadth of the book, this might be too much to ask.
Book Description
Standard texts and research in economics and finance ignore the absence of evidence from the analysis of real, unmassaged market data to support the notion of Adam Smith's stabilizing Invisible Hand. In stark contrast, this text introduces a new empirically-based model of financial market dynamics that explains the volatility of prices options correctly and clarifies the instability of financial markets. The emphasis is on understanding how real markets behave, not how they hypothetically 'should' behave.
Customer Reviews:
So what? .......2007-03-26
I am a 3rd year PhD student in Financial Economics, and although I should confess that I only browsed the book, not having fully read it (sorry about that...), I must say that this approach totally misses the target. I recommend anyone who is interested in knowing why to look up the following book review:
[...]
Have fun.
Good summary of the literature.......2007-01-10
I think this book is nicely written and covers a variety of subjects. What I missed while reading it was more applied examples.
McCauley complements Keynes and Mandelbrot.......2005-02-01
McCauley's(M) book definitely should be in the library of any technically trained (BA or BS degree in mathematics or statistics and a BA or BS in economics or finance) reader who is aware of the constant failure of neoclassical economics(and its modern variates such as rational expectations,real business cycle theory,monetarism,or supplyside economics), econometrics(Tinbergen,Frisch,Haavelmo and ,unfortunately,"Keynesian"econometricians like Modigliani,Tobin,Klein,and Solow) and financial analysts(Fama,Black,Merton,Scholes,Sharpe,Osborne,Markowitz and Cootner)to explain and forecast turning points in the business cycles of various countries and/or turning points in various financial markets(stock,commodity,real estate,currency,bond,money or derivatives)at any time in the last century,at least,using the assumption of normality(normal,lognormal,bivariate normal,multivariate normal,approximately normal,etc.).M presents a stochastic model based on the application of Green's Theorem to predict the future values of different options contracts(pp.180-192)that avoids the incorrect assumption of normality.M emphasizes changes in returns,as opposed to changes in prices a la Mandelbrot(pp.73-75).Again, the incorrect assumption of normality is avoided.This reviewer views these developments as occurring within the framework established by Mandelbrot no later than 1966.M is developing and improving aspects of Mandelbrot's general approach.However,there are three areas of M's book that need to be revised in a future edition.The first is his analysis of the classical-neoclassical concept of equilibrium and the process of adjustment involved over time.The argument made by neoclassical economists is that the economy( and all markets)is self equilibrating and always tending to or converging toward the optimal equilibrium point,although in point of fact,due to a constant set of external shocks,this equilibrium position is never reached.Thus, all short-run transactions may or may not be made at disequilibrium prices with no recontracting possible.The result,in the short run,is non optimal.However, in the long run,all of the losses and/or gains from such disequilibrium positions cancel or average out so that the resulting process can be analyzed "as if" the different markets were actually attaining equilibriums.Of course,all changes in market prices are assumed to be normally distributed around the equilibrium,market clearing price which is the average(arithmetic mean)of a normal probability distribution.This argument also is incorrect,but is much more difficult to refute since it is much more sophisticated ,using(misusing)the law of large numbers and the central limit theorem without ever actually examining the basic data.M needs to fine tune his basically sound critique to deal with the more sophisticated version of the neoclassical argument.If he does not,the neoclassical response will be that he does not understand microeconomic price theory.Second, Mandelbrot should not be bracketed with the likes of Markowitz Osborne,Sharpe,Black,Scholes,Merton,etc., on p.4 .Mandelbrot has, in fact,been clearly opposed,since the early 1960's, to the type of theoretical and statistical analysis and result that has been published by this group of economists and financial analysts.Third,M appears to have never read Keynes's A Treatise on Probability(1921;TP) or the 1939-40 exchange between Tinbergen and Keynes over the logical foundations of the basic econometric technique of multiple regression and correlation analysis ,as it regards forecasting of the business cycle.Keynes's complete argument can be found in chapter 17,pp.205-214,and chapters 29,30, 32,and 33 of the TP.Keynes always argued that,outside of the fields of life and physical science,the normal distribution was rather special and limited in application. The use of it required clearcut empirical testing of the data before normality could be assumed.Finally,Keynes's analytic tool in the General Theory(1936) is to show that the general case in macroeconomics is the existence of multiple stable equiibria.This describes the commodity or output market.The labor market is a function of changes in the commodity market.The labor market is in a state of constant disequilibrium,equilibrium only possibly occurring in the special case of a global optimum being obtained in the commodity market.M is correct that the analysis in most markets should be based on excess demand functions.Keynes arrived at this approach in 1936.A set of D=Z functions(functions clearly defined by Keynes in the GT and analyzed by Keynes in chapters 20 and 21 of the GT) define a locus of points that Keynes called the AGGREGATE SUPPLY CURVE.Only one of these points gives a global optimum.The economics profession has made a bloody mess of Keynes's mathematical analysis since the publication of the GT in 1936,constantly confusing the expected aggregate supply function,Z,with the aggregate supply curve,D=Z.M's treatment of Keynes is deficient and needs to be fixed in a later edition.A complete mathematical analysis of Keynes's theory of effective demand is contained in Brady(2004),"Essays on JM Keynes and..."
Some strengths, some weaknesses .......2004-12-28
The book serves up a very interesting and enlightening alternative to traditional economic thought in a variety of different contexts. I would certainly recommend it to any graduate student of physics or economics seeking to have a well rounded view the financial world.
The great weakness of this text is that the author seems to more than simply disagree with traditional economic theory, he despises it. That might not by itself be so great a weakness if the theory offered up in its stead were compelling, but, the author's passion notwithstanding, that is not the case here. The math aside, in tone and method this book reminds me very much of books authored by Intelligent Design advocates, seeking more to destroy the prominent competing theory than to present a coherent theory of its own.
Perhaps such passion is needed to get the neoclassical economists to pay attention. As in many things, I suspect the two schools of thought have much to teach one another.
On a more practical level, this is not a book for those who have not had a very solid grounding in mathematics, and likely unsuitable for all but the brightest, and most mathematically inclined, undergraduates.
Dynamics of Markets - Econophysics and Finance.......2004-12-10
Dynamics of Markets - Econophysics and Finance
by Joseph McCauley
reviewed by: Enrico Scalas
In 1720, Newton invested his money in the South Sea bubble and lost £20000, a lot of money in those days [1].
So, physicists do not always do it better in financial markets.
Having said that, let us now go on and consider the merits and limits of this book by Joseph McCauley.
The book is divided into nine chapters. Chapters 1, 3, 8 and 9 cover material from epistemology (ch. 1), probability theory (ch. 3), fluid dynamics (ch. 8), and the theory of computation (ch. 9). Chapters 2, 4, 5,6 and 7 are mainly devoted to economics and finance. Namely, chapter 2 critically reviews the general theory of equilibrium, chapter 4 is on the dynamics of markets, chapter 5 and 6 present portfolio selection theory and
option pricing, respectively, and, finally, chapter 7 is a criticism of thermodynamic analogies in finance.
The range of interests of the author is overwhelming and this book is the first attempt to put together many concepts taken from various disciplines in a coordinate view. I am a fan of this method and I much appreciate the effort of the author. However, this is also a limit, as the reader looking for recipes to price options or to select a suitable portfolio will be somehow disappointed. In the very same way, those looking for a
rational criticism of neo-classical assumptions in economics are likely to read the chapters on option pricing without great passion.
In a short review, it is impossible to take into account all the aspects of McCauley's book.
I will just discuss one: equilibrium in economics. But, before that, let me underline that this is the first book in Econophysics where everything in finance is done by explicitly formulating and calculating Green functions. Second, the author presents the European option price predictions in a closed algebraic form and, third, Gaussian returns play no role in the predictions fully based on the empirical distribution.
The author presents a nice criticism of the concept of equilibrium in economics which, in itself, is worth
buying and reading the book. The arguments are scattered throughout the book, as the author is interested
in discussing the behaviour of financial market. For economics and finance, the author provides convincing evidence that the only legitimate form of equilibrium is vanishing excess demand. But price fluctuations in actual financial markets cannot be effectively explained by a sequence of different economic equilibria determined by varying exogenous factors. Then, the only possibility is that excess demand is considered as a stochastic process leading to diffusive models for price (or return) dynamics. Thus, the use of the Green-function formalism in Finance is a natural and logical choice.
McCauley's discussion on equilibrium would have been helped by reference to Kaldor's 1972 paper on the irrelevance of equilibrium economics [2]. Kaldor's point of view coincides with the one of McCauley when he argues that ultimately theories must be confronted with the real world. In discussing the difference between an axiomatic theorem and a scientific theory, Kaldor quotes Einstein: << Physics constitute a logical system of thought which is in a state of evolution, whose basis cannot be distilled, as it were, from experience by an inductive
method, but can only be arrived at by free invention. The justification (truth content) of the system rests in the verification of the derived propositions by sense experiences. The skeptic will say: "it may well be true that this system of equations is reasonable from a logical standpoint. But it does not prove that it corresponds to nature". You are right, dear skeptic. Experience alone can decide on truth. >> [3]
Also in this book, as in many contemporary books, there are various misprints and the constant reference to wrong equation numbers is disturbing.
I think that this book can be read with profit both by physicists interested in complex systems and by economists interested in the principles of their discipline. Economists can always refer to Newton's example mentioned above, when they read in the book about the success of physicists in finance.
References
[1] C. Reed, "The Damn'd South Sea" Britain's greatest financial speculation and its unhappy ending, documented in a rich Harvard collection. Harvard Magazine, May-June 1999.
[2] N. Kaldor, "The Irrelevance of Equilibrium Economics", The Economic Journal, vol. 82, n. 328, 1237-1255, 1972.
[3] A. Einstein, "Ideas and Opinions", Gramercy; Reprint edition (December 12, 1988).
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- detailed into to SPSS 13 for novices, but getting tutorial files requires time
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A Handbook of Statistical Analyses Using SPSS
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SPSS Survival Manual
ASIN: 0387400834 |
Book Description
The second edition of this popular guide demonstrates the process of entering and analyzing data using the latest version of SPSS (12.0), and is also appropriate for those using earlier versions of SPSS. The book is easy to follow because all procedures are outlined in a step-by-step format designed for the novice user. Students are introduced to the rationale of statistical tests and detailed explanations of results are given through clearly annotated examples of SPSS output. Topics covered range from descriptive statistics through multiple regression analysis. In addition, this guide includes topics not typically covered in other books such as probability theory, interaction effects in analysis of variance, factor analysis, and scale reliability. This book can be used in two ways: as a stand-alone manual for students wishing to learn data analysis techniques using SPSS for Windows, or in research and statistics courses to be used with a basic statistics text. The book provides hands-on experience with actual data sets, helps students choose appropriate statistical tests, illustrates the meaning of results, and provides exercises to be completed for further practice or as homework assignments.
Customer Reviews:
detailed into to SPSS 13 for novices, but getting tutorial files requires time.......2005-10-15
This book is an introduction to SPSS 13 for beginners, which explains how to perform certain common operations step by step through hands-on exercises. The following topics are covered - user interface of SPSS, descriptive statistics (plots, medians, ranges, deviations), probability, inferential statistics, ANOVA, and exploratory factor analysis. Since the book is rather thin (193 pages of text + appendices), it goes through every topic quickly, which may be both good and bad depending on how much you want to learn and how much time you have...
The book is very detailed when it deals with description of the software, but it does require certain basic knowledge of statistics. The author does not even attempt to explain the statistical concepts used in the book. If you do not know what all these words mean, you are out of luck. I, for instance, know some basics and made it through the first half of the book in one day, but was totally lost when I got to chapter 10 where two-tailed tests, null hypotheses, etc., were introduced
What I found frustrating is that getting tutorial files requires a lot of work. About a third of them can be downloaded from Springer's web site in one package. The rest has to be downloaded individually, each file from its own web site, using URLs from the Appendix A. Many of them open as ASCII text in your web browser, after which you have to copy the data, paste into a text file, save it, then import in SPSS... Was it so difficult to combine them all in one convenient package, or use the same data file over and over again? I also suspect that some of the data which come from the web sites of scientific journals may require paid subscription, either individual or through the company/university, to access the files.
I actually read half of the book, skipping the examples which were not included into the Springer package, before I realized that instructions where to find them were in the Appendix A. The author did not care to expain in the beginning that this is where one should look for instructions where to find them. Very frustrating. To make things worse, some of the files (such as war.sav) are neither included in Springer package, nor there is a link to them in the Appendix A.
The good part is that each chapter ends with 4-5 simple exercises based on what was discussed in the text.
Summary: acceptable book for a beginner with some background in statistics, works well for a quick introduction, contains many hands-on exercises which are well described (but getting tutorial files from the web one at a time requires work). Since it covers only the basics, only selected options and capabilities of the SPSS package are presented. Not surprisingly, it does not contain enough reference information to substitute the manual.
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
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.
Book Description
Financial econometrics is a great success story in economics. Econometrics uses data and statistical inference methods, together with structural and descriptive modeling, to address rigorous economic problems. Its development within the world of finance is quite recent and has been paralleled by a fast expansion of financial markets and an increasing variety and complexity of financial products. This has fueled the demand for people with advanced econometrics skills.
For professionals and advanced graduate students pursuing greater expertise in econometric modeling, this is a superb guide to the field's frontier. With the goal of providing information that is absolutely up-to-date--essential in today's rapidly evolving financial environment--Gourieroux and Jasiak focus on methods related to foregoing research and those modeling techniques that seem relevant to future advances. They present a balanced synthesis of financial theory and statistical methodology. Recognizing that any model is necessarily a simplified image of reality and that econometric methods must be adapted and applied on a case-by-case basis, the authors employ a wide variety of data sampled at frequencies ranging from intraday to monthly. These data comprise time series representing both the European and North American markets for stocks, bonds, and foreign currencies. Practitioners are encouraged to keep a critical eye and are armed with graphical diagnostics to eradicate misspecification errors.
This authoritative, state-of-the-art reference text is ideal for upper-level graduate students, researchers, and professionals seeking to update their skills and gain greater facility in using econometric models. All will benefit from the emphasis on practical aspects of financial modeling and statistical inference. Doctoral candidates will appreciate the inclusion of detailed mathematical derivations of the deeper results as well as the more advanced problems concerning high-frequency data and risk control. By establishing a link between practical questions and the answers provided by financial and statistical theory, the book also addresses the needs of applied researchers employed by financial institutions.
Customer Reviews:
Sloppy.......2003-06-14
This book is not completely useless. It does tell you something about models that are used in finance. But the title misleads: there is a lot of description of models, but not much on estimation and inference procedures.
There is a general air of editorial sloppiness in a combination of factual errors, grammatical slips, awkward language and inscrutable logic. For example, on p.36 a process is defined to be I(1) iff its first difference is a weak white noise. And on p.172 there is this: "It is likely the asset prices to [sic] follow a nonstationary process, whereas the dividends and the excess gains are stationary processes." Huh? In the discussion of the consumption-based CAPM money and a price level are inexplicably included. Things of this kind recur through out.
A great introduction to financial econometrics.......2002-10-25
The book introduces a number of topics that usually can be only found in papers. For example, the treatment of the econometrics of derivatives, although not very extensive, is excellent. In this regard the book is vastly superior to Cambell and Lo's book. Overall, the book covers a wealth of topics in very accesible and concise manner. Probably, the best introduction to modern financial econometrics for practitioners.
Victor, try Google.......2002-04-15
(Forget the stars; I'm just posting the Table of Contents)
Table of Contents
Preface vii
1. Introduction 1
2. Univariate Linear Models: The AR(1) process and Its Extensions 17
3. Multivariate Linear Models: VARMA Representation 53
4. Simultaneity, Recursivty, and Casuality Analysis 81
5. Persistence and Cointegration 105
6. Conditional Heteroscedasticity: Nonlinear Autoaggressive Models, ARCH Models, Stochastic Volatility Models 117
7. Expection and Present Value Models 151
8. Intertemporal Behavior and the Method of Moments 173
9. Dynamic Factor Models 195
10. Dynamic Qualitative Proceses 219
11. Diffusion Models 241
12. Estimation of Diffusion Models 285
13. Econometrics of Derivatives 317
14. Dynamic Models for High-Freguency data 351
15. Market Indexes 247
16. Management of Extreme Risks 427
References 451
Index 477
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