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Author : William A. Barnett
Publisher : Cambridge University Press
Page : 512 pages
File Size : 33,52 MB
Release : 1991-06-28
Category : Business & Economics
ISBN : 9780521424318
Papers from a 1988 symposium on the estimation and testing of models that impose relatively weak restrictions on the stochastic behaviour of data.
Author : Joel L. Horowitz
Publisher : Springer Science & Business Media
Page : 211 pages
File Size : 39,25 MB
Release : 2012-12-06
Category : Mathematics
ISBN : 1461206219
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.
Author : Joel L. Horowitz
Publisher : Springer
Page : 276 pages
File Size : 14,86 MB
Release : 2009-08-07
Category : Business & Economics
ISBN : 9780387928692
Standard methods for estimating empirical models in economics and many other fields rely on strong assumptions about functional forms and the distributions of unobserved random variables. Often, it is assumed that functions of interest are linear or that unobserved random variables are normally distributed. Such assumptions simplify estimation and statistical inference but are rarely justified by economic theory or other a priori considerations. Inference based on convenient but incorrect assumptions about functional forms and distributions can be highly misleading. Nonparametric and semiparametric statistical methods provide a way to reduce the strength of the assumptions required for estimation and inference, thereby reducing the opportunities for obtaining misleading results. These methods are applicable to a wide variety of estimation problems in empirical economics and other fields, and they are being used in applied research with increasing frequency. The literature on nonparametric and semiparametric estimation is large and highly technical. This book presents the main ideas underlying a variety of nonparametric and semiparametric methods. It is accessible to graduate students and applied researchers who are familiar with econometric and statistical theory at the level taught in graduate-level courses in leading universities. The book emphasizes ideas instead of technical details and provides as intuitive an exposition as possible. Empirical examples illustrate the methods that are presented. This book updates and greatly expands the author’s previous book on semiparametric methods in econometrics. Nearly half of the material is new.
Author : Joel L. Horowitz
Publisher : Springer Science & Business Media
Page : 278 pages
File Size : 49,28 MB
Release : 2010-07-10
Category : Business & Economics
ISBN : 0387928707
Standard methods for estimating empirical models in economics and many other fields rely on strong assumptions about functional forms and the distributions of unobserved random variables. Often, it is assumed that functions of interest are linear or that unobserved random variables are normally distributed. Such assumptions simplify estimation and statistical inference but are rarely justified by economic theory or other a priori considerations. Inference based on convenient but incorrect assumptions about functional forms and distributions can be highly misleading. Nonparametric and semiparametric statistical methods provide a way to reduce the strength of the assumptions required for estimation and inference, thereby reducing the opportunities for obtaining misleading results. These methods are applicable to a wide variety of estimation problems in empirical economics and other fields, and they are being used in applied research with increasing frequency. The literature on nonparametric and semiparametric estimation is large and highly technical. This book presents the main ideas underlying a variety of nonparametric and semiparametric methods. It is accessible to graduate students and applied researchers who are familiar with econometric and statistical theory at the level taught in graduate-level courses in leading universities. The book emphasizes ideas instead of technical details and provides as intuitive an exposition as possible. Empirical examples illustrate the methods that are presented. This book updates and greatly expands the author’s previous book on semiparametric methods in econometrics. Nearly half of the material is new.
Author : Joel L Horowitz
Publisher :
Page : 220 pages
File Size : 50,90 MB
Release : 1998-04-01
Category :
ISBN : 9781461206224
Author : Adonis Yatchew
Publisher : Cambridge University Press
Page : 238 pages
File Size : 16,25 MB
Release : 2003-06-02
Category : Business & Economics
ISBN : 9780521012263
This book provides an accessible collection of techniques for analyzing nonparametric and semiparametric regression models. Worked examples include estimation of Engel curves and equivalence scales, scale economies, semiparametric Cobb-Douglas, translog and CES cost functions, household gasoline consumption, hedonic housing prices, option prices and state price density estimation. The book should be of interest to a broad range of economists including those working in industrial organization, labor, development, urban, energy and financial economics. A variety of testing procedures are covered including simple goodness of fit tests and residual regression tests. These procedures can be used to test hypotheses such as parametric and semiparametric specifications, significance, monotonicity and additive separability. Other topics include endogeneity of parametric and nonparametric effects, as well as heteroskedasticity and autocorrelation in the residuals. Bootstrap procedures are provided.
Author : David Ruppert
Publisher : Cambridge University Press
Page : 408 pages
File Size : 23,42 MB
Release : 2003-07-14
Category : Mathematics
ISBN : 9780521785167
Even experts on semiparametric regression should find something new here.
Author : Myoung-jae Lee
Publisher : Springer Science & Business Media
Page : 285 pages
File Size : 28,19 MB
Release : 2013-04-17
Category : Business & Economics
ISBN : 1475725507
In this book the author surveys new techniques in econometrics which may be used to analyse semiparametric models. As well as covering topics such as instrumental variable estimation, nonparametric density and regression function estimation and semiparametric limited dependent variable models, the book provides details of how these methods may be implemented using software.
Author : Aman Ullah
Publisher : Springer Science & Business Media
Page : 180 pages
File Size : 47,90 MB
Release : 2012-12-06
Category : Business & Economics
ISBN : 3642518486
Over the last three decades much research in empirical and theoretical economics has been carried on under various assumptions. For example a parametric functional form of the regression model, the heteroskedasticity, and the autocorrelation is always as sumed, usually linear. Also, the errors are assumed to follow certain parametric distri butions, often normal. A disadvantage of parametric econometrics based on these assumptions is that it may not be robust to the slight data inconsistency with the particular parametric specification. Indeed any misspecification in the functional form may lead to erroneous conclusions. In view of these problems, recently there has been significant interest in 'the semiparametric/nonparametric approaches to econometrics. The semiparametric approach considers econometric models where one component has a parametric and the other, which is unknown, a nonparametric specification (Manski 1984 and Horowitz and Neumann 1987, among others). The purely non parametric approach, on the other hand, does not specify any component of the model a priori. The main ingredient of this approach is the data based estimation of the unknown joint density due to Rosenblatt (1956). Since then, especially in the last decade, a vast amount of literature has appeared on nonparametric estimation in statistics journals. However, this literature is mostly highly technical and this may partly be the reason why very little is known about it in econometrics, although see Bierens (1987) and Ullah (1988).
Author : Jeffrey Racine
Publisher : Oxford University Press
Page : 562 pages
File Size : 26,91 MB
Release : 2014-04
Category : Business & Economics
ISBN : 0199857946
This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah, contains the latest research on nonparametric and semiparametric econometrics and statistics. Chapters by leading international econometricians and statisticians highlight the interface between econometrics and statistical methods for nonparametric and semiparametric procedures.