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Quantile regression for cross-sectio...

Uribe, Jorge M.

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Quantile regression for cross-sectional and time series data = applications in energy markets using R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantile regression for cross-sectional and time series data/ by Jorge M. Uribe, Montserrat Guillen.
Reminder of title:	applications in energy markets using R /
Author:	Uribe, Jorge M.
other author:	Guillen, Montserrat.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	x, 63 p. :ill., digital ;24 cm.
[NT 15003449]:	Why and When Should Quantile Regression Be Used?- A Case of Study: Modelling Energy Markets by the Means of Quantile Regression -- Quantile Regression: A Methodological Overview -- Cross-Sectional Quantile Regression -- Time Series Quantile Regression -- Goodness of Fit in Quantile Regression Models -- Novel Approaches in Quantile Regression -- What Have We Learned from Quantile Regression? Implications for Economics and Finance -- Appendix: Programs for Quantile Regression and Implementation in R.
Contained By:	Springer eBooks
Subject:	Quantile regression. -
Online resource:	https://doi.org/10.1007/978-3-030-44504-1
ISBN:	9783030445041

Quantile regression for cross-sectional and time series data = applications in energy markets using R /
Uribe, Jorge M.

Quantile regression for cross-sectional and time series dataapplications in energy markets using R /[electronic resource] :by Jorge M. Uribe, Montserrat Guillen. - Cham :Springer International Publishing :2020. - x, 63 p. :ill., digital ;24 cm. - SpringerBriefs in finance,2193-1720. - SpringerBriefs in finance..

Why and When Should Quantile Regression Be Used?- A Case of Study: Modelling Energy Markets by the Means of Quantile Regression -- Quantile Regression: A Methodological Overview -- Cross-Sectional Quantile Regression -- Time Series Quantile Regression -- Goodness of Fit in Quantile Regression Models -- Novel Approaches in Quantile Regression -- What Have We Learned from Quantile Regression? Implications for Economics and Finance -- Appendix: Programs for Quantile Regression and Implementation in R.

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.

ISBN: 9783030445041

Standard No.: 10.1007/978-3-030-44504-1doiSubjects--Topical Terms:

2148155
Quantile regression.

LC Class. No.: QA278.2 / .U753 2020

Dewey Class. No.: 519.536

Quantile regression for cross-sectional and time series data = applications in energy markets using R /
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520 $a 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.
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