Economic Applications Of Quantile Regression
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Economic Applications Of Quantile Regression
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Author : Bernd Fitzenberger
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Economic Applications Of Quantile Regression written by Bernd Fitzenberger and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Business & Economics categories.
Quantile regression has emerged as an essential statistical tool of contemporary empirical economics and biostatistics. Complementing classical least squares regression methods which are designed to estimate conditional mean models, quantile regression provides an ensemble of techniques for estimating families of conditional quantile models, thus offering a more complete view of the stochastic relationship among variables. This volume collects 12 outstanding empirical contributions in economics and offers an indispensable introduction to interpretation, implementation, and inference aspects of quantile regression.
Special Issue On Economic Applications Of Quantile Regression
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Author : Badi H. Baltagi
language : en
Publisher:
Release Date : 2001
Special Issue On Economic Applications Of Quantile Regression written by Badi H. Baltagi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
Special Issue On Economic Applications Of Quantile Regression 2 0
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Author : Bernd Fitzenberger
language : en
Publisher:
Release Date : 2022
Special Issue On Economic Applications Of Quantile Regression 2 0 written by Bernd Fitzenberger and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Economic Applications Of Quantile Regressions
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Author :
language : en
Publisher:
Release Date : 2001
Economic Applications Of Quantile Regressions written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
Quantile Regression For Cross Sectional And Time Series Data
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Author : Jorge M. Uribe
language : en
Publisher: Springer Nature
Release Date : 2020-03-30
Quantile Regression For Cross Sectional And Time Series Data written by Jorge M. Uribe and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-30 with Business & Economics categories.
This brief addresses the estimation of quantile regression models from a practical perspective, which will support researchers who need to use conditional quantile regression to measure economic relationships among a set of variables. It will also benefit students using the methodology for the first time, and practitioners at private or public organizations who are interested in modeling different fragments of the conditional distribution of a given variable. The book pursues a practical approach with reference to energy markets, helping readers learn the main features of the technique more quickly. Emphasis is placed on the implementation details and the correct interpretation of the quantile regression coefficients rather than on the technicalities of the method, unlike the approach used in the majority of the literature. All applications are illustrated with R.
Quantile Regression
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Author : Roger Koenker
language : en
Publisher: Cambridge University Press
Release Date : 2005-05-05
Quantile Regression written by Roger Koenker and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-05-05 with Business & Economics categories.
Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. The author has devoted more than 25 years of research to this topic. The methods in the analysis are illustrated with a variety of applications from economics, biology, ecology and finance. The treatment will find its core audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above.
Conditional Extremes And Near Extremes
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Author : Victor V. Chernozhukov
language : en
Publisher:
Release Date : 2000
Conditional Extremes And Near Extremes written by Victor V. Chernozhukov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with categories.
This paper develops a theory of high and low (extremal) quantile regression: the linear models, estimation, and inference. In particular, the models coherently combine the convenient, flexible linearity with the extreme-value-theoretic restrictions on tails and the general heteroscedasticity forms. Within these models, the limit laws for extremal quantile regression statistics are obtained under the rank conditions (experiments) constructed to reflect the extremal or rare nature of tail events. An inference framework is discussed. The results apply to cross-section (and possibly dependent) data. The applications, ranging from the analysis of babies' very low birth weights, (S, s) models, tail analysis in heteroscedastic regression models, outlier-robust inference in auction models, and decision-making under extreme uncertainty, provide the motivation and applications of this theory. Keywords: Quantile regression, extreme value theory, tail analysis, (S, s) models, auctions, price search, Extreme Risk. JEL Classifications: C13, C14, C21, C41, C51, C53, D21, D44, D81.
Quantile Regression
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Author : Lingxin Hao
language : en
Publisher: SAGE
Release Date : 2007-04-18
Quantile Regression written by Lingxin Hao and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-18 with Business & Economics categories.
Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao and Naiman show, in their application of quantile regression to empirical research, how this model yields a more complete understanding of inequality. Inequality is a perennial concern in the social sciences, and recently there has been much research in health inequality as well. Major software packages have also gradually implemented quantile regression. Quantile Regression will be of interest not only to the traditional social science market but other markets such as the health and public health related disciplines. Key Features: Establishes a natural link between quantile regression and inequality studies in the social sciences Contains clearly defined terms, simplified empirical equations, illustrative graphs, empirical tables and graphs from examples Includes computational codes using statistical software popular among social scientists Oriented to empirical research
Quantile Regression
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Author : Cristina Davino
language : en
Publisher: John Wiley & Sons
Release Date : 2013-12-31
Quantile Regression written by Cristina Davino and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-31 with Mathematics categories.
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and followed by applications using real data. Quantile Regression: Presents a complete treatment of quantile regression methods, including, estimation, inference issues and application of methods. Delivers a balance between methodolgy and application Offers an overview of the recent developments in the quantile regression framework and why to use quantile regression in a variety of areas such as economics, finance and computing. Features a supporting website (www.wiley.com/go/quantile_regression) hosting datasets along with R, Stata and SAS software code. Researchers and PhD students in the field of statistics, economics, econometrics, social and environmental science and chemistry will benefit from this book.
Inference For Extremal Conditional Quantile Models With An Application To Market And Birthweight Risks
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Author : Victor Chernozhukov
language : en
Publisher:
Release Date : 2011
Inference For Extremal Conditional Quantile Models With An Application To Market And Birthweight Risks written by Victor Chernozhukov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many economic and financial applications, such as conditional value-at-risk, production efficiency, and adjustment bands in (S, s) models. In this paper we provide feasible inference tools for extremal conditional quantile models that rely upon extreme value approximations to the distribution of self-normalized quantile regression statistics. The methods are simple to implement and can be of independent interest even in the non-regression case. We illustrate the results with two empirical examples analyzing extreme fluctuations of a stock return and extremely low percentiles of live infants' birth weights in the range between 250 and 1500 grams.