Semiparametric Methods in Econometrics
Semiparametric Methods in Econometrics

Author: Joel L. Horowitz

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 211

ISBN-13: 1461206219

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Many econometric models contain unknown functions as well as finite- dimensional parameters. Examples of such unknown functions are the distribution function of an unobserved random variable or a transformation of an observed variable. Econometric methods for estimating population parameters in the presence of unknown functions are called "semiparametric." During the past 15 years, much research has been carried out on semiparametric econometric models that are relevant to empirical economics. This book synthesizes the results that have been achieved for five important classes of models. The book is aimed at graduate students in econometrics and statistics as well as professionals who are not experts in semiparametic methods. The usefulness of the methods will be illustrated with applications that use real data.

Milton Friedman
Milton Friedman

Author: Robert A. Cord

Publisher: Oxford University Press

Published: 2016-05-20

Total Pages: 832

ISBN-13: 0191009423

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Milton Friedman is widely regarded as one of the most influential economists of the twentieth century. Although he made many important contributions to both economic theory and policy - most clearly demonstrated by his development of and support for monetarism - he was also active in various spheres of public policy, where he more often than not pursued his championing of the free market and liberty. This volume assesses the importance of the full range of Friedman's ideas, from his work on methodology in economics, his highly innovative consumption theory, and his extensive research on monetary economics, to his views on contentious social and political issues such as education, conscription, and drugs. It also presents personal recollections of Friedman by some of those who knew him, both as students and colleagues, and offers new evidence on Friedman's interactions with other noted economists, including George Stigler and Lionel Robbins. The volume provides readers with an up to date account of Friedman's work and continuing influence and will help to inform and stimulate further research across a variety of areas, including macroeconomics, the history of economic thought, as well as the development and different uses of public policy. With contributions from a stellar cast, this book will be invaluable to academics and students alike.

Efficient and Adaptive Estimation for Semiparametric Models
Efficient and Adaptive Estimation for Semiparametric Models

Author: Peter J. Bickel

Publisher: Springer

Published: 1998-06-01

Total Pages: 588

ISBN-13: 0387984739

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This book deals with estimation in situations in which there is believed to be enough information to model parametrically some, but not all of the features of a data set. Such models have arisen in a wide context in recent years, and involve new nonlinear estimation procedures. Statistical models of this type are directly applicable to fields such as economics, epidemiology, and astronomy.

Nonparametric and Semiparametric Models
Nonparametric and Semiparametric Models

Author: Wolfgang Karl Härdle

Publisher: Springer Science & Business Media

Published: 2012-08-27

Total Pages: 317

ISBN-13: 364217146X

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The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: 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.

Introduction to Empirical Processes and Semiparametric Inference
Introduction to Empirical Processes and Semiparametric Inference

Author: Michael R. Kosorok

Publisher: Springer Science & Business Media

Published: 2007-12-29

Total Pages: 482

ISBN-13: 0387749780

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Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.

Estimation in Semiparametric Models
Estimation in Semiparametric Models

Author: Johann Pfanzagl

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 116

ISBN-13: 1461233968

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Assume one has to estimate the mean J x P( dx) (or the median of P, or any other functional t;;(P)) on the basis ofi.i.d. observations from P. Ifnothing is known about P, then the sample mean is certainly the best estimator one can think of. If P is known to be the member of a certain parametric family, say {Po: {) E e}, one can usually do better by estimating {) first, say by {)(n)(.~.), and using J XPo(n)(;r.) (dx) as an estimate for J xPo(dx). There is an "intermediate" range, where we know something about the unknown probability measure P, but less than parametric theory takes for granted. Practical problems have always led statisticians to invent estimators for such intermediate models, but it usually remained open whether these estimators are nearly optimal or not. There was one exception: The case of "adaptivity", where a "nonparametric" estimate exists which is asymptotically optimal for any parametric submodel. The standard (and for a long time only) example of such a fortunate situation was the estimation of the center of symmetry for a distribution of unknown shape.

Semiparametric Regression
Semiparametric Regression

Author: David Ruppert

Publisher: Cambridge University Press

Published: 2003-07-14

Total Pages: 410

ISBN-13: 9780521785167

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Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion. The authors make liberal use of graphics and examples plus case studies taken from environmental, financial, and other applications. They include practical advice on implementation and pointers to relevant software. The 2003 book is suitable as a textbook for students with little background in regression as well as a reference book for statistically oriented scientists such as biostatisticians, econometricians, quantitative social scientists, epidemiologists, with a good working knowledge of regression and the desire to begin using more flexible semiparametric models. Even experts on semiparametric regression should find something new here.

Semiparametric Theory and Missing Data
Semiparametric Theory and Missing Data

Author: Anastasios Tsiatis

Publisher: Springer Science & Business Media

Published: 2007-01-15

Total Pages: 392

ISBN-13: 0387373454

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This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.

Asymptotic Statistics
Asymptotic Statistics

Author: A. W. van der Vaart

Publisher: Cambridge University Press

Published: 2000-06-19

Total Pages: 470

ISBN-13: 9780521784504

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This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. Suitable as a graduate or Master s level statistics text, this book will also give researchers an overview of the latest research in asymptotic statistics.