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Nonparametric Econometrics

Author : Qi Li
Publisher : Princeton University Press
Page : 769 pages
File Size : 14,10 MB
Release : 2011-10-09
Category : Business & Economics
ISBN : 1400841062

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A comprehensive, up-to-date textbook on nonparametric methods for students and researchers 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.

Applied Nonparametric Econometrics

Author : Daniel J. Henderson
Publisher : Cambridge University Press
Page : 381 pages
File Size : 48,99 MB
Release : 2015-01-19
Category : Business & Economics
ISBN : 110701025X

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The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignores the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls.

Nonparametric Econometrics

Author : Qi Li
Publisher : Princeton University Press
Page : 768 pages
File Size : 38,7 MB
Release : 2023-07-18
Category : Business & Economics
ISBN : 0691248087

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A comprehensive, up-to-date textbook on nonparametric methods for students and researchers 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.

Introduction to Nonparametric Estimation

Author : Alexandre B. Tsybakov
Publisher : Springer Science & Business Media
Page : 222 pages
File Size : 23,76 MB
Release : 2008-10-22
Category : Mathematics
ISBN : 0387790527

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Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field.

An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics

Author : Jeffrey S. Racine
Publisher : Cambridge University Press
Page : 436 pages
File Size : 44,71 MB
Release : 2019-06-27
Category : Business & Economics
ISBN : 1108757286

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Interest in nonparametric methodology has grown considerably over the past few decades, stemming in part from vast improvements in computer hardware and the availability of new software that allows practitioners to take full advantage of these numerically intensive methods. This book is written for advanced undergraduate students, intermediate graduate students, and faculty, and provides a complete teaching and learning course at a more accessible level of theoretical rigor than Racine's earlier book co-authored with Qi Li, Nonparametric Econometrics: Theory and Practice (2007). The open source R platform for statistical computing and graphics is used throughout in conjunction with the R package np. Recent developments in reproducible research is emphasized throughout with appendices devoted to helping the reader get up to speed with R, R Markdown, TeX and Git.

Nonparametric Econometrics

Author : Adrian Pagan
Publisher : Cambridge University Press
Page : 446 pages
File Size : 10,13 MB
Release : 1999-06-28
Category : Business & Economics
ISBN : 0521355648

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Covering the vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the last five decades, this book will be useful for first year graduate courses in econometrics.

Applied Nonparametric Regression

Author : Wolfgang Härdle
Publisher : Cambridge University Press
Page : 356 pages
File Size : 34,10 MB
Release : 1990
Category : Business & Economics
ISBN : 9780521429504

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This is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable.

Semiparametric and Nonparametric Methods in Econometrics

Author : Joel L. Horowitz
Publisher : Springer
Page : 276 pages
File Size : 31,24 MB
Release : 2009-08-07
Category : Business & Economics
ISBN : 9780387928692

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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.

Nonparametric and Semiparametric Models

Author : Wolfgang Karl Härdle
Publisher : Springer Science & Business Media
Page : 317 pages
File Size : 46,82 MB
Release : 2012-08-27
Category : Mathematics
ISBN : 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.

A Distribution-Free Theory of Nonparametric Regression

Author : László Györfi
Publisher : Springer Science & Business Media
Page : 662 pages
File Size : 25,53 MB
Release : 2006-04-18
Category : Mathematics
ISBN : 0387224424

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This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.