Economic Applications Of Quantile Regression PDF Download
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| Author | : Bernd Fitzenberger |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 325 |
| Release | : 2013-03-09 |
| Genre | : Business & Economics |
| ISBN | : 3662115921 |
Download Economic Applications of Quantile Regression Book in PDF, ePub and Kindle
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.
| Author | : Bernd Fitzenberger |
| Publisher | : |
| Total Pages | : |
| Release | : 2022 |
| Genre | : |
| ISBN | : |
Download Special Issue on Economic Applications of Quantile Regression 2.0 Book in PDF, ePub and Kindle
| Author | : Roger Koenker |
| Publisher | : Cambridge University Press |
| Total Pages | : 367 |
| Release | : 2005-05-05 |
| Genre | : Business & Economics |
| ISBN | : 1139444719 |
Download Quantile Regression Book in PDF, ePub and Kindle
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.
| Author | : Badi H. Baltagi |
| Publisher | : |
| Total Pages | : 330 |
| Release | : 2001 |
| Genre | : |
| ISBN | : |
Download Special Issue on Economic Applications of Quantile Regression Book in PDF, ePub and Kindle
| Author | : Jorge M. Uribe |
| Publisher | : Springer Nature |
| Total Pages | : 63 |
| Release | : 2020-03-30 |
| Genre | : Business & Economics |
| ISBN | : 3030445046 |
Download Quantile Regression for Cross-Sectional and Time Series Data Book in PDF, ePub and Kindle
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.
| Author | : |
| Publisher | : |
| Total Pages | : 324 |
| Release | : 2001 |
| Genre | : |
| ISBN | : |
Download Economic Applications of Quantile Regressions Book in PDF, ePub and Kindle
| Author | : Cristina Davino |
| Publisher | : John Wiley & Sons |
| Total Pages | : 288 |
| Release | : 2013-10-24 |
| Genre | : Mathematics |
| ISBN | : 1118752716 |
Download Quantile Regression Book in PDF, ePub and Kindle
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.
| Author | : Roger Koenker |
| Publisher | : CRC Press |
| Total Pages | : 739 |
| Release | : 2017-10-12 |
| Genre | : Mathematics |
| ISBN | : 1351646567 |
Download Handbook of Quantile Regression Book in PDF, ePub and Kindle
Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments. The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.
| Author | : Roger Koenker |
| Publisher | : CRC Press |
| Total Pages | : 463 |
| Release | : 2017-10-12 |
| Genre | : Mathematics |
| ISBN | : 1498725295 |
Download Handbook of Quantile Regression Book in PDF, ePub and Kindle
Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments. The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.
| Author | : Marilena Furno |
| Publisher | : John Wiley & Sons |
| Total Pages | : 311 |
| Release | : 2018-07-18 |
| Genre | : Mathematics |
| ISBN | : 111886364X |
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Contains an overview of several technical topics of Quantile Regression Volume two of Quantile Regression offers an important guide for applied researchers that draws on the same example-based approach adopted for the first volume. The text explores topics including robustness, expectiles, m-quantile, decomposition, time series, elemental sets and linear programming. Graphical representations are widely used to visually introduce several issues, and to illustrate each method. All the topics are treated theoretically and using real data examples. Designed as a practical resource, the book is thorough without getting too technical about the statistical background. The authors cover a wide range of QR models useful in several fields. The software commands in R and Stata are available in the appendixes and featured on the accompanying website. The text: Provides an overview of several technical topics such as robustness of quantile regressions, bootstrap and elemental sets, treatment effect estimators Compares quantile regression with alternative estimators like expectiles, M-estimators and M-quantiles Offers a general introduction to linear programming focusing on the simplex method as solving method for the quantile regression problem Considers time-series issues like non-stationarity, spurious regressions, cointegration, conditional heteroskedasticity via quantile regression Offers an analysis that is both theoretically and practical Presents real data examples and graphical representations to explain the technical issues Written for researchers and students in the fields of statistics, economics, econometrics, social and environmental science, this text offers guide to the theory and application of quantile regression models.
