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:
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
Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers.
Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data--nominal and ordinal--in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory.
This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types--continuous, nominal, and ordinal--within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables.
Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.
Book Description
This book presents the contemporary statistical methods and theory of nonlinear time series analysis. The principal focus is on nonparametric and semiparametric techniques developed in the last decade. It covers the techniques for modelling in state-space, in frequency-domain as well as in time-domain. To reflect the integration of parametric and nonparametric methods in analyzing time series data, the book also presents an up-to-date exposure of some parametric nonlinear models, including ARCH/GARCH models and threshold models. A compact view on linear ARMA models is also provided. Data arising in real applications are used throughout to show how nonparametric approaches may help to reveal local structure in high-dimensional data. Important technical tools are also introduced. The book will be useful for graduate students, application-oriented time series analysts, and new and experienced researchers. It will have the value both within the statistical community and across a broad spectrum of other fields such as econometrics, empirical finance, population biology and ecology. The prerequisites are basic courses in probability and statistics. Jianqing Fan, coauthor of the highly regarded book Local Polynomial Modeling, is Professor of Statistics at the University of North Carolina at Chapel Hill and the Chinese University of Hong Kong. His published work on nonparametric modeling, nonlinear time series, financial econometrics, analysis of longitudinal data, model selection, wavelets and other aspects of methodological and theoretical statistics has been recognized with the Presidents' Award from the Committee of Presidents of Statistical Societies, the Hettleman Prize for Artistic and Scholarly Achievement from the University of North Carolina, and by his election as a fellow of the American Statistical Association and the Institute of Mathematical Statistics. Qiwei Yao is Professor of Statistics at the London School of Economics and Political Science. He is an elected member of the International Statistical Institute, and has served on the editorial boards for the Journal of the Royal Statistical Society (Series B) and the Australian and New Zealand Journal of Statistics.
Customer Reviews:
Well used already!.......2006-08-31
This is an excellent monograph. The authors have provided an up-to-date analysis of parametric and nonparametric methods with a comprehensive bibliography. The book is very readible. The authors combine elements of descriptive overview, nontrivial examples and theorems / proofs.
Book Description
The theory of empirical processes provides valuable tools for the development of asymptotic theory in (nonparametric) statistical models, and makes it possible to give a unified treatment of various models. This book reveals the relation between the asymptotic behavior of M-estimators and the complexity of parameter space, using entropy as a measure of complexity, presenting tools and methods to analyze nonparametric, and in some cases, semiparametric methods. Graduate students and professionals in statistics, as well as those interested in applications, e.g. to econometrics, medical statistics, etc., will welcome this treatment.
Book Description
Applied Nonparametric Regression brings together in one place the techniques for regression curve smoothing involving more than one variable. The computer and the development of interactive graphics programs has made curve estimation popular. This volume focuses on the applications and practical problems of two central aspects of curve smoothing: the choice of smoothing parameters and the construction of confidence bounds. The methods covered in this text have numerous applications in many areas using statistical analysis. Examples are drawn from economics--such as the estimation of Engel curves--as well as other disciplines including medicine and engineering. For practical applications of these methods a computing environment for exploratory Regression--XploRe--is described.
Customer Reviews:
Very clear and readable account of nonparametric regression.......2001-04-11
This book deals with nonparametric regression, mainly kernel regression, though other estimators are also covered. This is a very clear and succinct discussion of the theory and application of this very important area of modern statistics. This book is one of the first few books on the subject (I saw a preprint of this book while a graduate student and bought a copy when it first appeared). Kernel estimators are known to be less efficient and suffer from boundary effects when the underlying regression is very smooth (twice differentiable). Local polynomial regression is the natural remedy and has experienced rapid developments in last 10 years. Fan and Gijbel's book in 1996 has detailed discussions on local polynomial estimation, an area which I also publish a few papers. But this book still contains substantial materials not found elsewhere. For example, this book has excellent discussion of nonparametric methods in econometric applications. I highly recommend it for its good introduction and as a valuable reference book on this subject.
Book Description
The concept of nonparametric smoothing is a central idea in statistics that aims to simultaneously estimate and modes the underlying structure. The book considers high dimensional objects, as density functions and regression. The semiparametric modeling technique compromises the two aims, flexibility and simplicity of statistical procedures, by introducing partial parametric components. These components allow to match structural conditions like e.g. linearity in some variables and may be used to model the influence of discrete variables.
The aim of this monograph is to present the statistical and mathematical principles of smoothing with a focus on applicable techniques. The necessary mathematical treatment is easily understandable and a wide variety of interactive smoothing examples are given.
The book does naturally split into two parts: Nonparametric models (histogram, kernel density estimation, nonparametric regression) and semiparametric models (generalized regression, single index models, generalized partial linear models, additive and generalized additive models). The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers.
The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.
Book Description
This collection of papers delivered at the fifth international Symposium in Economic Theory and Econometrics in 1988 is devoted to recent advances in the estimation and testing of models that impose relatively weak restrictions on the stochastic behavior of data. Particularly in highly nonlinear models, empirical results are very sensitive to the choice of the parametric form of the distribution of the observable variables, and often nonparametric and semiparametric models are a preferable alternative. Methods and applications that do not require strong parametric assumptions for their validity, that are based on kernels and on series expansions, and methods for independent and dependent observations, are investigated and developed in these essays by renowned econometricians.
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Nonparametric Comparative Statics and Stability
Douglas Hale ,
George Lady ,
John Maybee , and
James Quirk
Manufacturer: Princeton University Press
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ASIN: 0691006903 |
Book Description
The authors, leading researchers in the fields of mathematical economics and methodology, present the first comprehensive synthesis of literature on qualitative and other nonparametric techniques, which are important elements of comparative statics and stability analysis in economic theory. The topics covered show how to assess the comparative statics and stability of economic models without a precise quantitative knowledge of all model components. Applications of the analysis range from determining refutable hypotheses from theory to auditing the solutions of large, computer-based systems.
This book discusses in depth the methodology involved in a nonparametric analysis of many neoclassical economic models. Constituting a virtually self-contained manual on such analysis, it provides detailed derivation of necessary and sufficient conditions for the existence of restrictive comparative statics and stability results for a range of specified models. Further, algorithms for applying certain of these conditions are given, with examples, as well as the underlying mathematical approach taken.
A large body of research is unified covering issues that have been dealt with piecemeal in scattered but important journal articles by the authors and others. The book will prove invaluable to mathematical economists, mathematicians specializing in matrix or graph theory, applied economists working with large-scale economic models, and advanced students of economics.
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The Art of Semiparametrics (Contributions to Statistics)
Manufacturer: Physica-Verlag Heidelberg
ProductGroup: Book
Binding: Paperback
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ASIN: 3790817007 |
Book Description
This selection of articles has emerged from different works presented at the conference "The Art of Semiparametrics" celebrated in 2003 in Berlin. The idea was to bring together junior and senior researchers but also practitioners working on semiparametric statistics in rather different fields. The meeting succeeded in welcoming a group that presents a broad range of areas where research on, respectively with, semiparametric methods is going on. It contains mathematical statistics, econometrics, finance, business statistics, etc. and thus combines theoretical contributions with more applied and partly even empirical studies. Although each article represents an original contribution to its own field, they all are written in a self-contained way to be read also by non-experts of the particular topic. This volume therefore offers a collection of individual works that together show the actual large spectrum of semiparametric statistics.
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