Quantile Regression Methods

Bernd Fitzenberger, Ralf Andreas Wilke

Publikation: Bidrag til bog/antologi/rapportBidrag til bog/antologiForskningpeer review

Resumé

Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even if the mean regression model does not.
We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based on a cross-section sample. We summarize various important extensions of the model including the nonlinear quantile regression model, censored quantile regression, and quantile regression for time-series data. We also discuss a number of more recent extensions of the quantile regression model to censored data, duration data, and endogeneity, and we describe how quantile regression can be used for decomposition analysis. Finally, we identify several key issues, which should be addressed by future research, and we provide an overview of quantile regression implementations in major statistics software. Our treatment of the topic is based on the perspective of applied researchers using quantile regression in their empirical work.
OriginalsprogEngelsk
TitelEmerging Trends in the Social and Behavioral Sciences : An Interdisciplinary, Searchable, and Linkable Resource
RedaktørerRobert A. Scott , Stephen M. Kosslyn
Antal sider18
Udgivelses stedHoboken, N.J.
ForlagWiley
Publikationsdato2015
ISBN (Trykt)9781452216454
ISBN (Elektronisk)9781118900772
DOI
StatusUdgivet - 2015

Bibliografisk note

Published online: 15 May 2015

Emneord

  • Quantile Regression
  • Conditional quantiles
  • Check function
  • Equivariance of quantiles
  • Censored quantile regression
  • Time-series quantile regression
  • Panel data quantile regression
  • Endogeneity
  • Decomposition analysis with quantile regression

Citer dette

Fitzenberger, B., & Wilke, R. A. (2015). Quantile Regression Methods. I R. A. Scott , & S. M. Kosslyn (red.), Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource Hoboken, N.J.: Wiley. https://doi.org/10.1002/9781118900772.etrds0269
Fitzenberger, Bernd ; Wilke, Ralf Andreas. / Quantile Regression Methods. Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource. red. / Robert A. Scott ; Stephen M. Kosslyn. Hoboken, N.J. : Wiley, 2015.
@inbook{07e4b786204840769ffa2c7f4553f872,
title = "Quantile Regression Methods",
abstract = "Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even if the mean regression model does not.We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based on a cross-section sample. We summarize various important extensions of the model including the nonlinear quantile regression model, censored quantile regression, and quantile regression for time-series data. We also discuss a number of more recent extensions of the quantile regression model to censored data, duration data, and endogeneity, and we describe how quantile regression can be used for decomposition analysis. Finally, we identify several key issues, which should be addressed by future research, and we provide an overview of quantile regression implementations in major statistics software. Our treatment of the topic is based on the perspective of applied researchers using quantile regression in their empirical work.",
keywords = "Quantile Regression, Conditional quantiles, Check function, Equivariance of quantiles, Censored quantile regression, Time-series quantile regression, Panel data quantile regression, Endogeneity, Decomposition analysis with quantile regression",
author = "Bernd Fitzenberger and Wilke, {Ralf Andreas}",
note = "Published online: 15 May 2015",
year = "2015",
doi = "10.1002/9781118900772.etrds0269",
language = "English",
isbn = "9781452216454",
editor = "{Scott }, {Robert A.} and Kosslyn, {Stephen M.}",
booktitle = "Emerging Trends in the Social and Behavioral Sciences",
publisher = "Wiley",

}

Fitzenberger, B & Wilke, RA 2015, Quantile Regression Methods. i RA Scott & SM Kosslyn (red), Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource. Wiley, Hoboken, N.J. https://doi.org/10.1002/9781118900772.etrds0269

Quantile Regression Methods. / Fitzenberger, Bernd; Wilke, Ralf Andreas.

Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource. red. / Robert A. Scott ; Stephen M. Kosslyn. Hoboken, N.J. : Wiley, 2015.

Publikation: Bidrag til bog/antologi/rapportBidrag til bog/antologiForskningpeer review

TY - CHAP

T1 - Quantile Regression Methods

AU - Fitzenberger, Bernd

AU - Wilke, Ralf Andreas

N1 - Published online: 15 May 2015

PY - 2015

Y1 - 2015

N2 - Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even if the mean regression model does not.We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based on a cross-section sample. We summarize various important extensions of the model including the nonlinear quantile regression model, censored quantile regression, and quantile regression for time-series data. We also discuss a number of more recent extensions of the quantile regression model to censored data, duration data, and endogeneity, and we describe how quantile regression can be used for decomposition analysis. Finally, we identify several key issues, which should be addressed by future research, and we provide an overview of quantile regression implementations in major statistics software. Our treatment of the topic is based on the perspective of applied researchers using quantile regression in their empirical work.

AB - Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even if the mean regression model does not.We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based on a cross-section sample. We summarize various important extensions of the model including the nonlinear quantile regression model, censored quantile regression, and quantile regression for time-series data. We also discuss a number of more recent extensions of the quantile regression model to censored data, duration data, and endogeneity, and we describe how quantile regression can be used for decomposition analysis. Finally, we identify several key issues, which should be addressed by future research, and we provide an overview of quantile regression implementations in major statistics software. Our treatment of the topic is based on the perspective of applied researchers using quantile regression in their empirical work.

KW - Quantile Regression

KW - Conditional quantiles

KW - Check function

KW - Equivariance of quantiles

KW - Censored quantile regression

KW - Time-series quantile regression

KW - Panel data quantile regression

KW - Endogeneity

KW - Decomposition analysis with quantile regression

U2 - 10.1002/9781118900772.etrds0269

DO - 10.1002/9781118900772.etrds0269

M3 - Book chapter

SN - 9781452216454

BT - Emerging Trends in the Social and Behavioral Sciences

A2 - Scott , Robert A.

A2 - Kosslyn, Stephen M.

PB - Wiley

CY - Hoboken, N.J.

ER -

Fitzenberger B, Wilke RA. Quantile Regression Methods. I Scott RA, Kosslyn SM, red., Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource. Hoboken, N.J.: Wiley. 2015 https://doi.org/10.1002/9781118900772.etrds0269